Tilataso

Data-kansiossa on aineisto.


# load data
setwd("~/GitHub/tilataso")
library(readr)
tilat<-read.csv(file="kategoriset.csv", header=TRUE)

Kuvailevat

Valitsen muutaman jatkuvan muuttujan ja muutoin valitsen ne, joissa on alle 6 kategoriaa. Yhteenveto muuttujista:

colnames(tilat)[ apply(tilat, 2, anyNA) ]
## [1] "VAR00003"           "TII_alusta_5_laatu" "TII_lelukomm"      
## [4] "POR_pr_viemar"      "VAR00001"
tilat<-tilat[ , apply(tilat, 2, function(x) !any(is.na(x)))]


summaryKable(tilat[,1:218]) %>% 
  kable("html", align = "rrr", caption = "Data variable summary") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px")
Data variable summary
Min 1st Q Median Mean 3rd Q Max
Haastrooli_1OmEiosall_2OmOsall_3Esimies 1.000 2.000 2.000 1.884 2.000 3.000
Tuotsuunta 1.000 1.000 1.000 1.488 2.000 2.000
Karjut_astsiem 0.000 0.000 0.000 0.419 0.000 6.000
Tautsu 0.000 0.000 1.000 0.698 1.000 1.000
Tautsuok 0.000 0.000 0.000 0.488 1.000 1.000
Tautsu_012 0.000 0.000 1.000 1.093 2.000 2.000
Siilotkat 0.000 1.000 1.000 0.907 1.000 1.000
Tuhoei 0.000 1.000 1.000 0.814 1.000 1.000
Eikulkuih 0.000 0.000 1.000 0.628 1.000 1.000
Eikulkuel 0.000 0.000 1.000 0.698 1.000 1.000
Suojvar 1.000 1.000 1.000 1.000 1.000 1.000
Suojvarpuh 0.000 1.000 1.000 0.977 1.000 1.000
Kadetpesu 0.000 0.000 1.000 0.698 1.000 1.000
Toimsiis 0.000 1.000 1.000 0.953 1.000 1.000
Saappesu 0.000 0.000 1.000 0.721 1.000 1.000
Lasthu 0.000 1.000 1.000 0.837 1.000 1.000
Teurkuski_0paaseesikalaan_1eipaase 0.000 0.000 1.000 0.698 1.000 1.000
JOU_kertayt_0ei 0.000 0.000 0.000 0.163 0.000 1.000
JOU_tuotvaiherill_0ei 0.000 0.000 1.000 0.721 1.000 1.000
JOU_pesu_0ei 0.000 0.000 0.000 0.140 0.000 1.000
JOU_pesuaine_0ei 0.000 0.000 0.000 0.093 0.000 1.000
JOU_desinf_liu_0ei_1liuos_2kuiva 0.000 0.000 0.000 0.674 0.000 12.000
JOU_tyhjana_mi1vrk_0ei 0.000 0.000 0.000 0.302 1.000 1.000
PORSOSASTO_kertayt_0ei 0.000 0.000 0.000 0.419 1.000 1.000
PORS_tuotvaiherill_0ei 0.000 1.000 1.000 0.767 1.000 1.000
PORS_pesu_0ei 0.000 1.000 1.000 0.767 1.000 1.000
PORS_pesuaine_0ei 0.000 0.000 0.000 0.233 0.000 1.000
PORS_desinf_0ei_1LIU_2KUIVA 0.000 1.000 1.000 2.302 2.000 12.000
PORS_tyhjana_mi1vr_0ei 0.000 0.000 1.000 0.605 1.000 1.000
Raa_0ei_1kontti_2huone 0.000 1.000 1.000 2.000 1.000 12.000
Raa_auto_hakee_0ei 0.000 0.000 1.000 0.628 1.000 1.000
Raa_viilea_0ei 0.000 1.000 1.000 0.884 1.000 1.000
Raa_tuhoelain_1eipaase_0paaseesic 0.000 0.000 1.000 0.605 1.000 1.000
Tuhoelmerkkeja_0kylla_1ei 0.000 0.000 0.000 0.233 0.000 1.000
Lintuja_0kylla_1ei 0.000 1.000 1.000 0.767 1.000 1.000
Tuho_ohjelma 0.000 0.000 0.000 0.116 0.000 1.000
kissoja0on1ei 0.000 0.000 0.000 0.372 1.000 1.000
Kotielain_sikalaan_0kylla_1ei 0.000 1.000 1.000 0.791 1.000 1.000
Vesi_1kunn_0oma 0.000 0.000 1.000 0.628 1.000 1.000
Ery 1.000 1.000 1.000 1.000 1.000 1.000
Parvo 1.000 1.000 1.000 1.000 1.000 1.000
Koli 0.000 1.000 1.000 0.953 1.000 1.000
Sirko 0.000 0.000 0.000 0.302 1.000 1.000
ClC 0.000 0.000 0.000 0.070 0.000 1.000
ClA 0.000 0.000 0.000 0.000 0.000 0.000
SI 0.000 0.000 0.000 0.093 0.000 1.000
APP 0.000 0.000 0.000 0.116 0.000 1.000
Loisaika_1ennenpors_2_porskars 1.000 1.000 1.000 1.372 2.000 2.000
Uusiryh 1.000 2.000 2.000 2.000 2.000 4.000
Ton_tiheys_1aina_2jaetaan 1.000 1.000 1.000 1.093 1.000 2.000
Yhdistaggrtmp_1eiongelma_2tmp_3eitmp 1.000 2.000 2.000 4.349 3.000 12.000
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann 0.000 0.000 1.000 0.767 1.000 2.000
maitokuume 0.000 0.000 1.000 0.512 1.000 1.000
metriitti 0.000 0.000 0.000 0.442 1.000 1.000
valuttelu 0.000 0.000 0.000 0.116 0.000 1.000
mastiitti 0.000 0.000 0.000 0.233 0.000 1.000
ontuma 0.000 0.000 1.000 0.721 1.000 1.000
syomattomyys 0.000 0.000 1.000 0.512 1.000 1.000
kuume 0.000 0.000 0.000 0.140 0.000 1.000
loukkaantuminen 0.000 0.000 0.000 0.372 1.000 1.000
AB_rutiinilaak 0.000 0.000 0.000 0.140 0.000 1.000
Oksitosiini_rutiinisti 0.000 0.000 0.000 0.395 1.000 1.000
Kaynnistys_rutiinisti 0.000 0.000 0.000 0.093 0.000 1.000
NSAID_porsituksessa_rutiini 0.000 0.000 0.000 0.233 0.000 1.000
OMATENSIKOT_0EI_1KYLLa 0.000 0.000 1.000 0.651 1.000 1.000
Ensikk_valisiirtkars_ennensiem 0.000 0.000 0.000 0.395 1.000 1.000
Ensikk_kiihruok 0.000 0.000 0.000 0.372 1.000 1.000
Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars 0.000 1.000 1.000 0.953 1.000 2.000
siemika 7.000 8.000 8.000 8.070 8.000 9.500
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk 1.000 3.000 3.000 2.884 3.000 4.000
Kiimantark_ryhmakaytt 0.000 1.000 1.000 0.884 1.000 1.000
Kiimantarkalkaa_vrkvier 0.000 0.000 1.000 1.302 1.000 5.000
Kiimamerk_emakonselka 0.000 1.000 1.000 0.860 1.000 1.000
Kiimantark_postsiem 0.000 1.000 1.000 0.953 1.000 1.000
Postsiem_ryhmakaytt_havainnointi 0.000 1.000 1.000 0.884 1.000 1.000
Tiin_ultra2 6.000 6.000 6.000 6.140 6.000 10.000
Tiin_ultra_1yhdesti_2kahdesti 0.000 1.000 1.000 1.000 1.000 2.000
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen 0.000 0.000 2.000 1.953 4.000 4.000
Pesantekomatmaara_1runsas_2jnkv_3niukka 1.000 2.000 2.000 2.093 2.000 3.000
Sisatutk_ennenoksitos 0.000 0.000 0.000 0.349 1.000 1.000
Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste 34.000 34.000 134.000 305.628 134.000 1234.000
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa 1.000 2.000 2.000 1.814 2.000 2.000
Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa 1.000 2.000 2.000 2.953 2.000 12.000
Yksilöll_ruokinta 0.000 0.000 1.000 0.721 1.000 1.000
AS_1ast_jout_samassa_2asteiole 1.000 2.000 2.000 1.837 2.000 2.000
AS_er_os_lkm 1.000 1.000 1.000 1.093 1.000 2.000
AS_em_kars 2.500 7.500 7.500 8.605 7.500 60.000
AS_karspit 3.310 5.940 5.940 6.195 5.940 20.000
AS_karslev 2.670 4.800 4.800 4.807 4.800 7.000
AS_meluton 0.000 1.000 1.000 0.907 1.000 1.000
AS_haittael_ei 0.000 1.000 1.000 0.860 1.000 1.000
AS_haittael_laatu 1.000 1.000 1.000 2.070 4.000 4.000
AS_ilma_aistin 0.000 0.000 0.000 0.186 0.000 1.000
AS_ilma_amm 0.000 0.000 0.000 0.186 0.000 1.000
AS_ilma_pöly 0.000 0.000 0.000 0.000 0.000 0.000
AS_ilma_muu 0.000 0.000 0.000 0.000 0.000 0.000
AS_kosteus 0.000 0.000 0.000 0.000 0.000 0.000
AS_valaistus 0.000 0.000 0.000 0.047 0.000 1.000
AS_alusta12345 1.000 1.000 1.000 2.791 1.000 12.000
AS_alusta_5_laatu 0.000 0.000 0.000 0.000 0.000 0.000
AS_latt_rakenne1234 12.000 13.000 13.000 12.837 13.000 13.000
AS_pr_ritila 0.000 0.000 0.000 4.279 0.000 41.000
AS_pr_viemar 0.000 0.000 0.000 0.000 0.000 0.000
AS_kuiv_mat12345 1.000 1.500 1.500 2.012 1.500 14.000
AS_kuiv_5_mika 0.000 3.000 3.000 2.884 3.000 4.000
AS_maara1234 0.000 4.000 4.000 3.698 4.000 4.000
AS_tonkimat123456 1.000 1.000 1.000 1.349 1.000 12.000
AS_tonkimat_6_mika 0.000 0.000 0.000 0.000 0.000 0.000
AS_mat_vaiht 0.000 1.000 1.000 0.953 1.000 1.000
AS_maara123 0.000 2.000 2.000 2.070 2.000 3.000
AS_annostelu1234 0.000 1.000 1.000 1.000 1.000 3.000
AS_lannanpoisto12 0.000 2.000 2.000 2.116 2.000 12.000
AS_rak_kunto 0.000 0.000 0.000 0.023 0.000 1.000
AS_latt_pitava 0.000 0.000 0.000 0.023 0.000 1.000
AS_sairkars 0.000 0.000 0.000 0.256 0.500 1.000
AS_sk_parempi 0.000 1.000 1.000 0.849 1.000 1.000
AS_sk_kiintea 0.000 0.000 0.000 0.000 0.000 0.000
AS_sk_kuivike 0.000 0.000 0.000 0.093 0.000 1.000
AS_sk_siisti 0.000 0.000 0.000 0.070 0.000 1.000
AS_sk_kuiva 0.000 0.000 0.000 0.035 0.000 1.000
AS_sk_syörauha 0.000 0.000 0.000 0.116 0.000 1.000
AS_sk_juorauha 0.000 0.000 0.000 0.116 0.000 1.000
AS_ruoklaite12345 0.000 4.000 4.000 3.814 4.000 4.000
AS_ruokpaikka 0.000 1.000 1.000 1.047 1.000 4.000
AS_ruokpuht 0.000 0.000 0.000 0.140 0.000 1.000
AS_juomalaite123 0.000 1.000 1.000 0.977 1.000 1.000
AS_juonalkm 0.222 1.000 1.000 1.011 1.000 2.250
AS_juomapuht 0.000 0.000 0.000 0.023 0.000 1.000
AS_juomatoim 0.000 0.000 0.000 0.000 0.000 0.000
AS_rauhallisuus123 0.000 1.000 1.000 0.953 1.000 1.000
AS_hoitotarveKE 1.000 1.000 1.000 1.442 2.000 2.000
AS_stereo 0.000 0.000 0.000 0.140 0.000 1.000
TII_1ast_jout_samassa_2asteiole 0.000 0.000 0.000 0.186 0.000 2.000
TII_valiseinat 0.000 0.000 0.000 0.488 0.000 16.000
TII_meluton 0.000 1.000 1.000 0.791 1.000 1.000
TII_haittael_ei 0.000 1.000 1.000 0.767 1.000 1.000
TII_ilma_aistin 0.000 0.000 0.000 0.116 0.000 1.000
TII_ilma_amm 0.000 0.000 0.000 0.140 0.000 1.000
TII_ilma_pöly 0.000 0.000 0.000 0.000 0.000 0.000
TII_ilma_muu 0.000 0.000 0.000 0.000 0.000 0.000
TII_kosteus 0.000 0.000 0.000 0.000 0.000 0.000
TII_valaistus 0.000 0.000 0.000 0.023 0.000 1.000
TII_alusta12345 1.000 1.000 1.000 1.000 1.000 1.000
TII_latt_rakenne1234 1.000 13.000 13.000 11.744 13.000 23.000
TII_pr_ritila 0.000 0.000 0.000 4.140 0.000 50.000
TII_pr_viemar 0.000 0.000 0.000 0.000 0.000 0.000
TII_kuiv_mat12345 1.000 2.000 2.000 3.721 2.000 15.000
TII_kuiv_5_mika 1.000 1.000 1.000 1.023 1.000 2.000
TII_maara1234 1.000 3.000 3.000 3.349 3.000 23.000
TII_tonkimat_6_mika 1.000 1.000 1.000 1.326 1.000 5.000
TII_lelu1234 2.000 4.000 4.000 4.395 4.000 24.000
TII_mat_vaiht 0.000 1.000 1.000 0.977 1.000 1.000
TII_maara123 1.000 2.000 2.000 1.930 2.000 3.000
TII_annostelu1234 1.000 1.000 1.000 1.163 1.000 4.000
TII_lannanpoisto12 1.000 1.000 1.000 2.535 5.000 5.000
TII_rak_kunto 0.000 0.000 0.000 0.047 0.000 1.000
TII_latt_pitava 0.000 0.000 0.000 0.070 0.000 1.000
TII_sairkars 0.000 1.000 1.000 0.907 1.000 1.000
TII_ruok_0nonlock_1lock 0.000 0.000 0.000 0.395 1.000 1.000
TII_ruokpuht 0.000 0.000 0.000 0.000 0.000 0.000
TII_juomalaite123 1.000 1.000 1.000 1.279 1.000 12.000
TII_juomapuht 0.000 0.000 0.000 0.000 0.000 0.000
TII_juomatoim 0.000 0.000 0.000 0.047 0.000 2.000
TII_rauhallisuus123 1.000 1.000 1.000 1.023 1.000 2.000
TII_hoitotarveKE 1.000 1.000 2.000 1.512 2.000 2.000
TII_stereo 0.000 0.000 0.000 0.093 0.000 1.000
POR_meluton 0.000 1.000 1.000 0.767 1.000 1.000
POR_haittael_ei 0.000 1.000 1.000 0.860 1.000 1.000
POR_haittael_laatu 1.000 1.000 1.000 2.186 4.000 4.000
POR_ilma_aistin 0.000 0.000 0.000 0.023 0.000 1.000
POR_ilma_amm 0.000 0.000 0.000 0.023 0.000 1.000
POR_ilma_pöly 0.000 0.000 0.000 0.000 0.000 0.000
POR_ilma_muu 0.000 0.000 0.000 0.000 0.000 0.000
POR_kosteus 0.000 0.000 0.000 0.000 0.000 0.000
POR_valaistus 0.000 0.000 0.000 0.058 0.000 1.000
POR_latt_rakenne1234 1.000 12.000 12.000 13.395 12.000 123.000
POR_pr_rako 0.000 0.000 0.000 0.884 0.000 38.000
POR_maara1234 2.000 3.000 3.000 2.953 3.000 4.000
POR_tonkimat_6_mika 1.000 1.000 1.000 1.442 1.000 5.000
POR_lelu1234 2.000 4.000 4.000 3.930 4.000 5.000
POR_lelukomm 1.000 1.000 1.000 1.140 1.000 4.000
POR_mat_vaiht 1.000 1.000 1.000 1.023 1.000 2.000
POR_maara123 1.000 2.000 2.000 2.000 2.000 3.000
POR_annostelu1234 1.000 1.000 1.000 1.233 1.000 4.000
POR_lannanpoisto12 1.000 2.000 2.000 1.907 2.000 2.000
POR_rak_kunto 0.000 0.000 0.000 0.070 0.000 1.000
POR_latt_pitava 0.000 0.000 0.000 0.070 0.000 1.000
POR_sairkars 1.000 1.000 1.000 1.860 3.000 5.000
POR_ruoklaite12345 2.000 2.500 2.500 3.012 2.500 25.000
POR_ruokpaikka 1.000 1.000 1.000 1.000 1.000 1.000
POR_ruokpuht 0.000 0.000 0.000 0.070 0.000 1.000
POR_juomalaite123 1.000 1.000 1.000 1.279 1.000 13.000
POR_juonalkm 1.000 1.000 1.000 1.000 1.000 1.000
POR_juomapuht 0.000 0.000 0.000 0.000 0.000 0.000
POR_juomatoim 0.000 0.000 0.000 0.023 0.000 1.000
POR_rauhallisuus123 1.000 1.000 1.000 1.000 1.000 1.000
Hajukarjut_per_emakko 0.000 0.010 0.010 0.013 0.015 0.060
TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv 0.000 2.000 3.000 2.884 4.000 4.000
TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme 0.000 1.000 2.000 1.884 3.000 3.000
AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv 0.000 1.000 2.000 2.047 3.000 4.000
AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme 0.000 1.000 1.000 1.442 2.000 3.000
POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv 0.000 2.000 3.000 2.698 3.500 4.000
POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme 0.000 1.500 2.000 1.791 2.000 3.000
Koulmax_1peru_2ops_3a_4amk_5yl 2.000 3.000 3.000 3.209 3.000 5.000
Stressi_1erpal_4jnkv 1.000 2.000 3.000 2.884 4.000 4.000
EMKUOLLJAKO 0.000 0.000 0.000 0.465 1.000 1.000
EMPOISJAKO 0.000 0.000 0.000 0.442 1.000 1.000
EMENKUOLLJAKO 0.000 0.000 0.000 0.419 1.000 1.000
EMENPOISJAKO 0.000 0.000 0.000 0.372 1.000 1.000
NIVEL_01 1.000 1.000 1.000 1.419 2.000 2.000
PAISE_01 1.000 1.000 1.000 1.419 2.000 2.000
MAKUU01 1.000 1.000 1.000 1.419 2.000 2.000
KOKO_01 1.000 1.000 1.000 1.419 2.000 2.000
OSA_01 1.000 1.000 1.000 1.419 2.000 2.000
JOKUHYLK_01 1.000 1.000 1.000 1.419 2.000 2.000
PLEUR_01 0.000 0.000 0.000 0.279 1.000 1.000
PNEUM_01 1.000 1.000 1.000 1.419 2.000 2.000
SAIRKARS_AST_TII 0.000 1.000 1.000 0.767 1.000 1.000
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.

tilatkat<-tilat[,1:218]%>%mutate_all(as.factor)

tilatkat$EMKUOL<-tilat$EMKUOLLJAKO
table1 = KreateTableOne(x=tilatkat, strata='EMKUOL')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by EMKUOL") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by EMKUOL
0 1 p test
n 23 20
Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) 0.431
1 4 ( 17.4) 6 ( 30.0)
2 17 ( 73.9) 11 ( 55.0)
3 2 ( 8.7) 3 ( 15.0)
Tuotsuunta = 2 (%) 14 ( 60.9) 7 ( 35.0) 0.165
Karjut_astsiem (%) 0.264
0 21 ( 91.3) 17 ( 85.0)
1 1 ( 4.3) 0 ( 0.0)
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
6 0 ( 0.0) 2 ( 10.0)
Tautsu = 1 (%) 15 ( 65.2) 15 ( 75.0) 0.716
Tautsuok = 1 (%) 9 ( 39.1) 12 ( 60.0) 0.289
Tautsu_012 (%) 0.733
0 8 ( 34.8) 5 ( 25.0)
1 7 ( 30.4) 6 ( 30.0)
2 8 ( 34.8) 9 ( 45.0)
Siilotkat = 1 (%) 21 ( 91.3) 18 ( 90.0) 1.000
Tuhoei = 1 (%) 16 ( 69.6) 19 ( 95.0) 0.081
Eikulkuih = 1 (%) 16 ( 69.6) 11 ( 55.0) 0.503
Eikulkuel = 1 (%) 14 ( 60.9) 16 ( 80.0) 0.303
Suojvar = 1 (%) 23 (100.0) 20 (100.0) NA
Suojvarpuh = 1 (%) 23 (100.0) 19 ( 95.0) 0.944
Kadetpesu = 1 (%) 14 ( 60.9) 16 ( 80.0) 0.303
Toimsiis = 1 (%) 22 ( 95.7) 19 ( 95.0) 1.000
Saappesu = 1 (%) 14 ( 60.9) 17 ( 85.0) 0.156
Lasthu = 1 (%) 19 ( 82.6) 17 ( 85.0) 1.000
Teurkuski_0paaseesikalaan_1eipaase = 1 (%) 17 ( 73.9) 13 ( 65.0) 0.763
JOU_kertayt_0ei = 1 (%) 3 ( 13.0) 4 ( 20.0) 0.840
JOU_tuotvaiherill_0ei = 1 (%) 16 ( 69.6) 15 ( 75.0) 0.956
JOU_pesu_0ei = 1 (%) 4 ( 17.4) 2 ( 10.0) 0.798
JOU_pesuaine_0ei = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
JOU_desinf_liu_0ei_1liuos_2kuiva (%) 0.406
0 20 ( 87.0) 18 ( 90.0)
1 0 ( 0.0) 1 ( 5.0)
2 2 ( 8.7) 0 ( 0.0)
12 1 ( 4.3) 1 ( 5.0)
JOU_tyhjana_mi1vrk_0ei = 1 (%) 5 ( 21.7) 8 ( 40.0) 0.333
PORSOSASTO_kertayt_0ei = 1 (%) 9 ( 39.1) 9 ( 45.0) 0.937
PORS_tuotvaiherill_0ei = 1 (%) 19 ( 82.6) 14 ( 70.0) 0.539
PORS_pesu_0ei = 1 (%) 17 ( 73.9) 16 ( 80.0) 0.913
PORS_pesuaine_0ei = 1 (%) 4 ( 17.4) 6 ( 30.0) 0.539
PORS_desinf_0ei_1LIU_2KUIVA (%) 0.623
0 5 ( 21.7) 4 ( 20.0)
1 9 ( 39.1) 10 ( 50.0)
2 7 ( 30.4) 3 ( 15.0)
12 2 ( 8.7) 3 ( 15.0)
PORS_tyhjana_mi1vr_0ei = 1 (%) 13 ( 56.5) 13 ( 65.0) 0.799
Raa_0ei_1kontti_2huone (%) 0.456
0 2 ( 8.7) 1 ( 5.0)
1 19 ( 82.6) 15 ( 75.0)
2 0 ( 0.0) 2 ( 10.0)
12 2 ( 8.7) 2 ( 10.0)
Raa_auto_hakee_0ei = 1 (%) 16 ( 69.6) 11 ( 55.0) 0.503
Raa_viilea_0ei = 1 (%) 21 ( 91.3) 17 ( 85.0) 0.868
Raa_tuhoelain_1eipaase_0paaseesic = 1 (%) 13 ( 56.5) 13 ( 65.0) 0.799
Tuhoelmerkkeja_0kylla_1ei = 1 (%) 5 ( 21.7) 5 ( 25.0) 1.000
Lintuja_0kylla_1ei = 1 (%) 19 ( 82.6) 14 ( 70.0) 0.539
Tuho_ohjelma = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
kissoja0on1ei (%) 0.005
0 19 ( 82.6) 7 ( 35.0)
0.5 0 ( 0.0) 2 ( 10.0)
1 4 ( 17.4) 11 ( 55.0)
Kotielain_sikalaan_0kylla_1ei = 1 (%) 17 ( 73.9) 17 ( 85.0) 0.606
Vesi_1kunn_0oma = 1 (%) 16 ( 69.6) 11 ( 55.0) 0.503
Ery = 1 (%) 23 (100.0) 20 (100.0) NA
Parvo = 1 (%) 23 (100.0) 20 (100.0) NA
Koli = 1 (%) 22 ( 95.7) 19 ( 95.0) 1.000
Sirko = 1 (%) 8 ( 34.8) 5 ( 25.0) 0.716
ClC = 1 (%) 1 ( 4.3) 2 ( 10.0) 0.900
ClA = 0 (%) 23 (100.0) 20 (100.0) NA
SI = 1 (%) 1 ( 4.3) 3 ( 15.0) 0.501
APP = 1 (%) 3 ( 13.0) 2 ( 10.0) 1.000
Loisaika_1ennenpors_2_porskars = 2 (%) 9 ( 39.1) 7 ( 35.0) 1.000
Uusiryh (%) 0.524
1 1 ( 4.3) 2 ( 10.0)
2 20 ( 87.0) 18 ( 90.0)
3 1 ( 4.3) 0 ( 0.0)
4 1 ( 4.3) 0 ( 0.0)
Ton_tiheys_1aina_2jaetaan = 2 (%) 4 ( 17.4) 0 ( 0.0) 0.152
Yhdistaggrtmp_1eiongelma_2tmp_3eitmp (%) 0.334
1 4 ( 17.4) 1 ( 5.0)
2 9 ( 39.1) 13 ( 65.0)
3 4 ( 17.4) 2 ( 10.0)
12 6 ( 26.1) 4 ( 20.0)
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) 0.142
0 10 ( 43.5) 6 ( 30.0)
1 12 ( 52.2) 9 ( 45.0)
2 1 ( 4.3) 5 ( 25.0)
maitokuume = 1 (%) 12 ( 52.2) 10 ( 50.0) 1.000
metriitti = 1 (%) 10 ( 43.5) 9 ( 45.0) 1.000
valuttelu = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
mastiitti = 1 (%) 4 ( 17.4) 6 ( 30.0) 0.539
ontuma = 1 (%) 15 ( 65.2) 16 ( 80.0) 0.461
syomattomyys = 1 (%) 10 ( 43.5) 12 ( 60.0) 0.438
kuume = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
loukkaantuminen = 1 (%) 10 ( 43.5) 6 ( 30.0) 0.551
AB_rutiinilaak = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
Oksitosiini_rutiinisti = 1 (%) 8 ( 34.8) 9 ( 45.0) 0.711
Kaynnistys_rutiinisti = 1 (%) 0 ( 0.0) 4 ( 20.0) 0.084
NSAID_porsituksessa_rutiini = 1 (%) 6 ( 26.1) 4 ( 20.0) 0.913
OMATENSIKOT_0EI_1KYLLa = 1 (%) 15 ( 65.2) 13 ( 65.0) 1.000
Ensikk_valisiirtkars_ennensiem = 1 (%) 8 ( 34.8) 9 ( 45.0) 0.711
Ensikk_kiihruok = 1 (%) 8 ( 34.8) 8 ( 40.0) 0.971
Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars (%) 0.401
0 2 ( 8.7) 2 ( 10.0)
1 19 ( 82.6) 18 ( 90.0)
2 2 ( 8.7) 0 ( 0.0)
siemika (%) 0.208
7 1 ( 4.3) 0 ( 0.0)
7.5 1 ( 4.3) 1 ( 5.0)
8 20 ( 87.0) 14 ( 70.0)
8.5 0 ( 0.0) 4 ( 20.0)
9.5 1 ( 4.3) 1 ( 5.0)
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) 0.386
1 1 ( 4.3) 0 ( 0.0)
2 3 ( 13.0) 2 ( 10.0)
3 17 ( 73.9) 18 ( 90.0)
4 2 ( 8.7) 0 ( 0.0)
Kiimantark_ryhmakaytt = 1 (%) 20 ( 87.0) 18 ( 90.0) 1.000
Kiimantarkalkaa_vrkvier (%) 0.264
0 7 ( 30.4) 5 ( 25.0)
1 14 ( 60.9) 9 ( 45.0)
3 0 ( 0.0) 3 ( 15.0)
4 0 ( 0.0) 1 ( 5.0)
5 2 ( 8.7) 2 ( 10.0)
Kiimamerk_emakonselka = 1 (%) 17 ( 73.9) 20 (100.0) 0.043
Kiimantark_postsiem = 1 (%) 21 ( 91.3) 20 (100.0) 0.532
Postsiem_ryhmakaytt_havainnointi = 1 (%) 20 ( 87.0) 18 ( 90.0) 1.000
Tiin_ultra2 (%) 0.364
6 22 ( 95.7) 19 ( 95.0)
8 1 ( 4.3) 0 ( 0.0)
10 0 ( 0.0) 1 ( 5.0)
Tiin_ultra_1yhdesti_2kahdesti (%) 0.533
0 5 ( 21.7) 2 ( 10.0)
1 15 ( 65.2) 14 ( 70.0)
2 3 ( 13.0) 4 ( 20.0)
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) 0.115
0 10 ( 43.5) 6 ( 30.0)
1 0 ( 0.0) 2 ( 10.0)
2 2 ( 8.7) 6 ( 30.0)
3 2 ( 8.7) 0 ( 0.0)
4 9 ( 39.1) 6 ( 30.0)
Pesantekomatmaara_1runsas_2jnkv_3niukka (%) 0.763
1 1 ( 4.3) 2 ( 10.0)
2 18 ( 78.3) 15 ( 75.0)
3 4 ( 17.4) 3 ( 15.0)
Sisatutk_ennenoksitos = 1 (%) 7 ( 30.4) 8 ( 40.0) 0.737
Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste (%) 0.570
34 8 ( 34.8) 7 ( 35.0)
124 2 ( 8.7) 0 ( 0.0)
134 8 ( 34.8) 9 ( 45.0)
234 1 ( 4.3) 0 ( 0.0)
1234 4 ( 17.4) 4 ( 20.0)
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) 19 ( 82.6) 16 ( 80.0) 1.000
Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa (%) 0.897
1 1 ( 4.3) 1 ( 5.0)
2 19 ( 82.6) 15 ( 75.0)
3 1 ( 4.3) 2 ( 10.0)
12 2 ( 8.7) 2 ( 10.0)
Yksilöll_ruokinta = 1 (%) 17 ( 73.9) 14 ( 70.0) 1.000
AS_1ast_jout_samassa_2asteiole = 2 (%) 18 ( 78.3) 18 ( 90.0) 0.531
AS_er_os_lkm = 2 (%) 2 ( 8.7) 2 ( 10.0) 1.000
AS_em_kars (%) 0.400
2.5 1 ( 4.3) 0 ( 0.0)
7 0 ( 0.0) 1 ( 5.0)
7.5 21 ( 91.3) 18 ( 90.0)
8 0 ( 0.0) 1 ( 5.0)
60 1 ( 4.3) 0 ( 0.0)
AS_karspit (%) 0.429
3.31 1 ( 4.3) 0 ( 0.0)
4.4 1 ( 4.3) 0 ( 0.0)
5.94 20 ( 87.0) 19 ( 95.0)
7 0 ( 0.0) 1 ( 5.0)
20 1 ( 4.3) 0 ( 0.0)
AS_karslev (%) 0.429
2.67 1 ( 4.3) 0 ( 0.0)
3.02 0 ( 0.0) 1 ( 5.0)
4.8 20 ( 87.0) 19 ( 95.0)
6.8 1 ( 4.3) 0 ( 0.0)
7 1 ( 4.3) 0 ( 0.0)
AS_meluton = 1 (%) 21 ( 91.3) 18 ( 90.0) 1.000
AS_haittael_ei = 1 (%) 20 ( 87.0) 17 ( 85.0) 1.000
AS_haittael_laatu (%) 0.637
1 15 ( 65.2) 11 ( 55.0)
2 1 ( 4.3) 1 ( 5.0)
3 1 ( 4.3) 0 ( 0.0)
4 6 ( 26.1) 8 ( 40.0)
AS_ilma_aistin = 1 (%) 3 ( 13.0) 5 ( 25.0) 0.540
AS_ilma_amm = 1 (%) 3 ( 13.0) 5 ( 25.0) 0.540
AS_ilma_pöly = 0 (%) 23 (100.0) 20 (100.0) NA
AS_ilma_muu = 0 (%) 23 (100.0) 20 (100.0) NA
AS_kosteus = 0 (%) 23 (100.0) 20 (100.0) NA
AS_valaistus = 1 (%) 1 ( 4.3) 1 ( 5.0) 1.000
AS_alusta12345 = 12 (%) 2 ( 8.7) 5 ( 25.0) 0.303
AS_alusta_5_laatu = 0 (%) 23 (100.0) 20 (100.0) NA
AS_latt_rakenne1234 = 13 (%) 21 ( 91.3) 15 ( 75.0) 0.303
AS_pr_ritila (%) 0.535
0 21 ( 91.3) 15 ( 75.0)
20 1 ( 4.3) 2 ( 10.0)
25 1 ( 4.3) 1 ( 5.0)
33 0 ( 0.0) 1 ( 5.0)
41 0 ( 0.0) 1 ( 5.0)
AS_pr_viemar = 0 (%) 23 (100.0) 20 (100.0) NA
AS_kuiv_mat12345 (%) 0.397
1 2 ( 8.7) 2 ( 10.0)
1.5 19 ( 82.6) 16 ( 80.0)
2 0 ( 0.0) 2 ( 10.0)
12 1 ( 4.3) 0 ( 0.0)
14 1 ( 4.3) 0 ( 0.0)
AS_kuiv_5_mika (%) 0.157
0 0 ( 0.0) 2 ( 10.0)
3 23 (100.0) 17 ( 85.0)
4 0 ( 0.0) 1 ( 5.0)
AS_maara1234 (%) 0.372
0 1 ( 4.3) 0 ( 0.0)
1 1 ( 4.3) 0 ( 0.0)
2 0 ( 0.0) 1 ( 5.0)
3 1 ( 4.3) 3 ( 15.0)
4 20 ( 87.0) 16 ( 80.0)
AS_tonkimat123456 (%) 0.364
1 22 ( 95.7) 19 ( 95.0)
5 0 ( 0.0) 1 ( 5.0)
12 1 ( 4.3) 0 ( 0.0)
AS_tonkimat_6_mika = 0 (%) 23 (100.0) 20 (100.0) NA
AS_mat_vaiht = 1 (%) 22 ( 95.7) 19 ( 95.0) 1.000
AS_maara123 (%) 0.054
0 0 ( 0.0) 1 ( 5.0)
2 18 ( 78.3) 19 ( 95.0)
3 5 ( 21.7) 0 ( 0.0)
AS_annostelu1234 (%) 0.639
0 1 ( 4.3) 1 ( 5.0)
1 21 ( 91.3) 19 ( 95.0)
3 1 ( 4.3) 0 ( 0.0)
AS_lannanpoisto12 (%) 0.524
0 1 ( 4.3) 0 ( 0.0)
1 1 ( 4.3) 2 ( 10.0)
2 20 ( 87.0) 18 ( 90.0)
12 1 ( 4.3) 0 ( 0.0)
AS_rak_kunto = 1 (%) 0 ( 0.0) 1 ( 5.0) 0.944
AS_latt_pitava = 1 (%) 1 ( 4.3) 0 ( 0.0) 1.000
AS_sairkars = 1 (%) 3 ( 13.0) 8 ( 40.0) 0.095
AS_sk_parempi (%) 0.538
0 3 ( 13.0) 3 ( 15.0)
0.5 0 ( 0.0) 1 ( 5.0)
1 20 ( 87.0) 16 ( 80.0)
AS_sk_kiintea = 0 (%) 23 (100.0) 20 (100.0) NA
AS_sk_kuivike (%) 0.891
0 20 ( 87.0) 18 ( 90.0)
0.5 1 ( 4.3) 1 ( 5.0)
1 2 ( 8.7) 1 ( 5.0)
AS_sk_siisti = 1 (%) 2 ( 8.7) 1 ( 5.0) 1.000
AS_sk_kuiva (%) 0.364
0 22 ( 95.7) 19 ( 95.0)
0.5 0 ( 0.0) 1 ( 5.0)
1 1 ( 4.3) 0 ( 0.0)
AS_sk_syörauha = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
AS_sk_juorauha = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
AS_ruoklaite12345 = 4 (%) 22 ( 95.7) 19 ( 95.0) 1.000
AS_ruokpaikka (%) 0.402
0 1 ( 4.3) 0 ( 0.0)
1 21 ( 91.3) 20 (100.0)
4 1 ( 4.3) 0 ( 0.0)
AS_ruokpuht = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
AS_juomalaite123 = 1 (%) 23 (100.0) 19 ( 95.0) 0.944
AS_juonalkm (%) 0.364
0.222222222222222 0 ( 0.0) 1 ( 5.0)
1 22 ( 95.7) 19 ( 95.0)
2.25 1 ( 4.3) 0 ( 0.0)
AS_juomapuht = 1 (%) 0 ( 0.0) 1 ( 5.0) 0.944
AS_juomatoim = 0 (%) 23 (100.0) 20 (100.0) NA
AS_rauhallisuus123 = 1 (%) 22 ( 95.7) 19 ( 95.0) 1.000
AS_hoitotarveKE = 2 (%) 9 ( 39.1) 10 ( 50.0) 0.683
AS_stereo = 1 (%) 4 ( 17.4) 2 ( 10.0) 0.798
TII_1ast_jout_samassa_2asteiole (%) 0.401
0 19 ( 82.6) 18 ( 90.0)
1 2 ( 8.7) 2 ( 10.0)
2 2 ( 8.7) 0 ( 0.0)
TII_valiseinat (%) 0.440
0 22 ( 95.7) 16 ( 80.0)
0.5 0 ( 0.0) 1 ( 5.0)
1 1 ( 4.3) 1 ( 5.0)
2.5 0 ( 0.0) 1 ( 5.0)
16 0 ( 0.0) 1 ( 5.0)
TII_meluton = 1 (%) 18 ( 78.3) 16 ( 80.0) 1.000
TII_haittael_ei = 1 (%) 19 ( 82.6) 14 ( 70.0) 0.539
TII_ilma_aistin = 1 (%) 1 ( 4.3) 4 ( 20.0) 0.263
TII_ilma_amm = 1 (%) 1 ( 4.3) 5 ( 25.0) 0.131
TII_ilma_pöly = 0 (%) 23 (100.0) 20 (100.0) NA
TII_ilma_muu = 0 (%) 23 (100.0) 20 (100.0) NA
TII_kosteus = 0 (%) 23 (100.0) 20 (100.0) NA
TII_valaistus = 1 (%) 1 ( 4.3) 0 ( 0.0) 1.000
TII_alusta12345 = 1 (%) 23 (100.0) 20 (100.0) NA
TII_latt_rakenne1234 (%) 0.624
1 2 ( 8.7) 3 ( 15.0)
12 2 ( 8.7) 2 ( 10.0)
13 19 ( 82.6) 14 ( 70.0)
23 0 ( 0.0) 1 ( 5.0)
TII_pr_ritila (%) 0.217
0 22 ( 95.7) 16 ( 80.0)
20 1 ( 4.3) 0 ( 0.0)
28 0 ( 0.0) 1 ( 5.0)
40 0 ( 0.0) 2 ( 10.0)
50 0 ( 0.0) 1 ( 5.0)
TII_pr_viemar = 0 (%) 23 (100.0) 20 (100.0) NA
TII_kuiv_mat12345 (%) 0.508
1 4 ( 17.4) 1 ( 5.0)
2 15 ( 65.2) 16 ( 80.0)
12 1 ( 4.3) 2 ( 10.0)
14 2 ( 8.7) 1 ( 5.0)
15 1 ( 4.3) 0 ( 0.0)
TII_kuiv_5_mika = 2 (%) 1 ( 4.3) 0 ( 0.0) 1.000
TII_maara1234 (%) 0.669
1 3 ( 13.0) 1 ( 5.0)
2 2 ( 8.7) 3 ( 15.0)
3 14 ( 60.9) 11 ( 55.0)
4 4 ( 17.4) 4 ( 20.0)
23 0 ( 0.0) 1 ( 5.0)
TII_tonkimat_6_mika (%) 0.440
1 22 ( 95.7) 16 ( 80.0)
2 0 ( 0.0) 1 ( 5.0)
3 0 ( 0.0) 1 ( 5.0)
4 0 ( 0.0) 1 ( 5.0)
5 1 ( 4.3) 1 ( 5.0)
TII_lelu1234 (%) 0.257
2 2 ( 8.7) 0 ( 0.0)
4 21 ( 91.3) 18 ( 90.0)
5 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
TII_mat_vaiht = 1 (%) 23 (100.0) 19 ( 95.0) 0.944
TII_maara123 (%) 0.844
1 3 ( 13.0) 1 ( 5.0)
1.5 1 ( 4.3) 1 ( 5.0)
2 18 ( 78.3) 17 ( 85.0)
3 1 ( 4.3) 1 ( 5.0)
TII_annostelu1234 (%) 0.550
1 22 ( 95.7) 18 ( 90.0)
2 0 ( 0.0) 1 ( 5.0)
4 1 ( 4.3) 1 ( 5.0)
TII_lannanpoisto12 (%) 0.168
1 15 ( 65.2) 8 ( 40.0)
2 1 ( 4.3) 0 ( 0.0)
3 1 ( 4.3) 4 ( 20.0)
4 1 ( 4.3) 0 ( 0.0)
5 5 ( 21.7) 8 ( 40.0)
TII_rak_kunto = 1 (%) 0 ( 0.0) 2 ( 10.0) 0.408
TII_latt_pitava = 1 (%) 1 ( 4.3) 2 ( 10.0) 0.900
TII_sairkars = 1 (%) 21 ( 91.3) 18 ( 90.0) 1.000
TII_ruok_0nonlock_1lock = 1 (%) 11 ( 47.8) 6 ( 30.0) 0.379
TII_ruokpuht = 0 (%) 23 (100.0) 20 (100.0) NA
TII_juomalaite123 (%) 0.364
1 22 ( 95.7) 19 ( 95.0)
2 1 ( 4.3) 0 ( 0.0)
12 0 ( 0.0) 1 ( 5.0)
TII_juomapuht = 0 (%) 23 (100.0) 20 (100.0) NA
TII_juomatoim = 2 (%) 1 ( 4.3) 0 ( 0.0) 1.000
TII_rauhallisuus123 = 2 (%) 0 ( 0.0) 1 ( 5.0) 0.944
TII_hoitotarveKE = 2 (%) 11 ( 47.8) 11 ( 55.0) 0.870
TII_stereo = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
POR_meluton = 1 (%) 18 ( 78.3) 15 ( 75.0) 1.000
POR_haittael_ei = 1 (%) 19 ( 82.6) 18 ( 90.0) 0.798
POR_haittael_laatu (%) 0.383
1 15 ( 65.2) 10 ( 50.0)
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
4 7 ( 30.4) 9 ( 45.0)
POR_ilma_aistin = 1 (%) 1 ( 4.3) 0 ( 0.0) 1.000
POR_ilma_amm = 1 (%) 1 ( 4.3) 0 ( 0.0) 1.000
POR_ilma_pöly = 0 (%) 23 (100.0) 20 (100.0) NA
POR_ilma_muu = 0 (%) 23 (100.0) 20 (100.0) NA
POR_kosteus = 0 (%) 23 (100.0) 20 (100.0) NA
POR_valaistus (%) 0.234
0 21 ( 91.3) 19 ( 95.0)
0.5 0 ( 0.0) 1 ( 5.0)
1 2 ( 8.7) 0 ( 0.0)
POR_latt_rakenne1234 (%) 0.387
1 2 ( 8.7) 0 ( 0.0)
2 2 ( 8.7) 1 ( 5.0)
12 18 ( 78.3) 18 ( 90.0)
13 0 ( 0.0) 1 ( 5.0)
123 1 ( 4.3) 0 ( 0.0)
POR_pr_rako = 38 (%) 0 ( 0.0) 1 ( 5.0) 0.944
POR_maara1234 (%) 0.020
2 1 ( 4.3) 7 ( 35.0)
3 17 ( 73.9) 12 ( 60.0)
4 5 ( 21.7) 1 ( 5.0)
POR_tonkimat_6_mika (%) 0.307
1 21 ( 91.3) 15 ( 75.0)
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
4 1 ( 4.3) 3 ( 15.0)
5 0 ( 0.0) 1 ( 5.0)
POR_lelu1234 (%) 0.216
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 2 ( 10.0)
4 22 ( 95.7) 17 ( 85.0)
5 0 ( 0.0) 1 ( 5.0)
POR_lelukomm (%) 0.423
1 20 ( 87.0) 20 (100.0)
2 1 ( 4.3) 0 ( 0.0)
3 1 ( 4.3) 0 ( 0.0)
4 1 ( 4.3) 0 ( 0.0)
POR_mat_vaiht = 2 (%) 1 ( 4.3) 0 ( 0.0) 1.000
POR_maara123 (%) 0.401
1 2 ( 8.7) 0 ( 0.0)
2 20 ( 87.0) 19 ( 95.0)
3 1 ( 4.3) 1 ( 5.0)
POR_annostelu1234 (%) 0.763
1 19 ( 82.6) 18 ( 90.0)
2 2 ( 8.7) 1 ( 5.0)
3 1 ( 4.3) 1 ( 5.0)
4 1 ( 4.3) 0 ( 0.0)
POR_lannanpoisto12 = 2 (%) 20 ( 87.0) 19 ( 95.0) 0.704
POR_rak_kunto = 1 (%) 1 ( 4.3) 2 ( 10.0) 0.900
POR_latt_pitava = 1 (%) 3 ( 13.0) 0 ( 0.0) 0.283
POR_sairkars (%) 0.674
1 14 ( 60.9) 15 ( 75.0)
2 1 ( 4.3) 0 ( 0.0)
3 2 ( 8.7) 2 ( 10.0)
4 5 ( 21.7) 3 ( 15.0)
5 1 ( 4.3) 0 ( 0.0)
POR_ruoklaite12345 (%) 0.257
2 0 ( 0.0) 2 ( 10.0)
2.5 21 ( 91.3) 18 ( 90.0)
3 1 ( 4.3) 0 ( 0.0)
25 1 ( 4.3) 0 ( 0.0)
POR_ruokpaikka = 1 (%) 23 (100.0) 20 (100.0) NA
POR_ruokpuht = 1 (%) 0 ( 0.0) 3 ( 15.0) 0.185
POR_juomalaite123 = 13 (%) 0 ( 0.0) 1 ( 5.0) 0.944
POR_juonalkm = 1 (%) 23 (100.0) 20 (100.0) NA
POR_juomapuht = 0 (%) 23 (100.0) 20 (100.0) NA
POR_juomatoim = 1 (%) 0 ( 0.0) 1 ( 5.0) 0.944
POR_rauhallisuus123 = 1 (%) 23 (100.0) 20 (100.0) NA
Hajukarjut_per_emakko (%) 0.560
0 3 ( 13.0) 4 ( 20.0)
0.01 13 ( 56.5) 12 ( 60.0)
0.02 3 ( 13.0) 2 ( 10.0)
0.03 4 ( 17.4) 1 ( 5.0)
0.06 0 ( 0.0) 1 ( 5.0)
TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv (%) 0.339
0 1 ( 4.3) 3 ( 15.0)
1 0 ( 0.0) 2 ( 10.0)
2 4 ( 17.4) 4 ( 20.0)
3 6 ( 26.1) 4 ( 20.0)
4 12 ( 52.2) 7 ( 35.0)
TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.246
0 1 ( 4.3) 3 ( 15.0)
1 3 ( 13.0) 6 ( 30.0)
2 12 ( 52.2) 6 ( 30.0)
3 7 ( 30.4) 5 ( 25.0)
AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv (%) 0.019
0 4 ( 17.4) 1 ( 5.0)
1 0 ( 0.0) 7 ( 35.0)
2 12 ( 52.2) 5 ( 25.0)
3 4 ( 17.4) 5 ( 25.0)
4 3 ( 13.0) 2 ( 10.0)
AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.227
0 4 ( 17.4) 1 ( 5.0)
1 9 ( 39.1) 13 ( 65.0)
2 6 ( 26.1) 2 ( 10.0)
3 4 ( 17.4) 4 ( 20.0)
POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv (%) 0.194
0 2 ( 8.7) 1 ( 5.0)
1 1 ( 4.3) 2 ( 10.0)
2 7 ( 30.4) 2 ( 10.0)
3 10 ( 43.5) 7 ( 35.0)
4 3 ( 13.0) 8 ( 40.0)
POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.851
0 1 ( 4.3) 1 ( 5.0)
1 6 ( 26.1) 3 ( 15.0)
2 14 ( 60.9) 14 ( 70.0)
3 2 ( 8.7) 2 ( 10.0)
Koulmax_1peru_2ops_3a_4amk_5yl (%) 0.613
2 3 ( 13.0) 2 ( 10.0)
3 15 ( 65.2) 13 ( 65.0)
4 4 ( 17.4) 2 ( 10.0)
5 1 ( 4.3) 3 ( 15.0)
Stressi_1erpal_4jnkv (%) 0.846
1 4 ( 17.4) 2 ( 10.0)
2 4 ( 17.4) 4 ( 20.0)
3 8 ( 34.8) 6 ( 30.0)
4 7 ( 30.4) 8 ( 40.0)
EMKUOLLJAKO = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
EMPOISJAKO = 1 (%) 5 ( 21.7) 14 ( 70.0) 0.004
EMENKUOLLJAKO = 1 (%) 1 ( 4.3) 17 ( 85.0) <0.001
EMENPOISJAKO = 1 (%) 3 ( 13.0) 13 ( 65.0) 0.001
NIVEL_01 = 2 (%) 8 ( 34.8) 10 ( 50.0) 0.485
PAISE_01 = 2 (%) 8 ( 34.8) 10 ( 50.0) 0.485
MAKUU01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
KOKO_01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
OSA_01 = 2 (%) 9 ( 39.1) 9 ( 45.0) 0.937
JOKUHYLK_01 = 2 (%) 6 ( 26.1) 12 ( 60.0) 0.053
PLEUR_01 = 1 (%) 4 ( 17.4) 8 ( 40.0) 0.191
PNEUM_01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
SAIRKARS_AST_TII = 1 (%) 15 ( 65.2) 18 ( 90.0) 0.120
EMKUOL (mean (sd)) 0.00 (0.00) 1.00 (0.00) <0.001

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.

tilatkat2<-tilatkat
tilatkat2$EMPOIS<-tilat$EMPOISJAKO
table2 = KreateTableOne(x=tilatkat2, strata='EMPOIS')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by EMPOIS") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by EMPOIS
0 1 p test
n 24 19
Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) 0.311
1 4 ( 16.7) 6 ( 31.6)
2 18 ( 75.0) 10 ( 52.6)
3 2 ( 8.3) 3 ( 15.8)
Tuotsuunta = 2 (%) 13 ( 54.2) 8 ( 42.1) 0.632
Karjut_astsiem (%) 0.245
0 22 ( 91.7) 16 ( 84.2)
1 1 ( 4.2) 0 ( 0.0)
2 1 ( 4.2) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.3)
6 0 ( 0.0) 2 ( 10.5)
Tautsu = 1 (%) 15 ( 62.5) 15 ( 78.9) 0.405
Tautsuok = 1 (%) 9 ( 37.5) 12 ( 63.2) 0.172
Tautsu_012 (%) 0.473
0 9 ( 37.5) 4 ( 21.1)
1 7 ( 29.2) 6 ( 31.6)
2 8 ( 33.3) 9 ( 47.4)
Siilotkat = 1 (%) 22 ( 91.7) 17 ( 89.5) 1.000
Tuhoei = 1 (%) 20 ( 83.3) 15 ( 78.9) 1.000
Eikulkuih = 1 (%) 16 ( 66.7) 11 ( 57.9) 0.785
Eikulkuel = 1 (%) 17 ( 70.8) 13 ( 68.4) 1.000
Suojvar = 1 (%) 24 (100.0) 19 (100.0) NA
Suojvarpuh = 1 (%) 23 ( 95.8) 19 (100.0) 1.000
Kadetpesu = 1 (%) 16 ( 66.7) 14 ( 73.7) 0.870
Toimsiis = 1 (%) 23 ( 95.8) 18 ( 94.7) 1.000
Saappesu = 1 (%) 18 ( 75.0) 13 ( 68.4) 0.892
Lasthu = 1 (%) 19 ( 79.2) 17 ( 89.5) 0.622
Teurkuski_0paaseesikalaan_1eipaase = 1 (%) 18 ( 75.0) 12 ( 63.2) 0.613
JOU_kertayt_0ei = 1 (%) 4 ( 16.7) 3 ( 15.8) 1.000
JOU_tuotvaiherill_0ei = 1 (%) 18 ( 75.0) 13 ( 68.4) 0.892
JOU_pesu_0ei = 1 (%) 4 ( 16.7) 2 ( 10.5) 0.893
JOU_pesuaine_0ei = 1 (%) 3 ( 12.5) 1 ( 5.3) 0.777
JOU_desinf_liu_0ei_1liuos_2kuiva (%) 0.709
0 22 ( 91.7) 16 ( 84.2)
1 0 ( 0.0) 1 ( 5.3)
2 1 ( 4.2) 1 ( 5.3)
12 1 ( 4.2) 1 ( 5.3)
JOU_tyhjana_mi1vrk_0ei = 1 (%) 9 ( 37.5) 4 ( 21.1) 0.405
PORSOSASTO_kertayt_0ei = 1 (%) 8 ( 33.3) 10 ( 52.6) 0.336
PORS_tuotvaiherill_0ei = 1 (%) 17 ( 70.8) 16 ( 84.2) 0.504
PORS_pesu_0ei = 1 (%) 20 ( 83.3) 13 ( 68.4) 0.432
PORS_pesuaine_0ei = 1 (%) 5 ( 20.8) 5 ( 26.3) 0.953
PORS_desinf_0ei_1LIU_2KUIVA (%) 0.894
0 4 ( 16.7) 5 ( 26.3)
1 11 ( 45.8) 8 ( 42.1)
2 6 ( 25.0) 4 ( 21.1)
12 3 ( 12.5) 2 ( 10.5)
PORS_tyhjana_mi1vr_0ei = 1 (%) 14 ( 58.3) 12 ( 63.2) 0.994
Raa_0ei_1kontti_2huone (%) 0.844
0 1 ( 4.2) 2 ( 10.5)
1 20 ( 83.3) 14 ( 73.7)
2 1 ( 4.2) 1 ( 5.3)
12 2 ( 8.3) 2 ( 10.5)
Raa_auto_hakee_0ei = 1 (%) 17 ( 70.8) 10 ( 52.6) 0.364
Raa_viilea_0ei = 1 (%) 22 ( 91.7) 16 ( 84.2) 0.781
Raa_tuhoelain_1eipaase_0paaseesic = 1 (%) 15 ( 62.5) 11 ( 57.9) 1.000
Tuhoelmerkkeja_0kylla_1ei = 1 (%) 6 ( 25.0) 4 ( 21.1) 1.000
Lintuja_0kylla_1ei = 1 (%) 19 ( 79.2) 14 ( 73.7) 0.953
Tuho_ohjelma = 1 (%) 2 ( 8.3) 3 ( 15.8) 0.781
kissoja0on1ei (%) 0.051
0 18 ( 75.0) 8 ( 42.1)
0.5 0 ( 0.0) 2 ( 10.5)
1 6 ( 25.0) 9 ( 47.4)
Kotielain_sikalaan_0kylla_1ei = 1 (%) 20 ( 83.3) 14 ( 73.7) 0.693
Vesi_1kunn_0oma = 1 (%) 15 ( 62.5) 12 ( 63.2) 1.000
Ery = 1 (%) 24 (100.0) 19 (100.0) NA
Parvo = 1 (%) 24 (100.0) 19 (100.0) NA
Koli = 1 (%) 23 ( 95.8) 18 ( 94.7) 1.000
Sirko = 1 (%) 8 ( 33.3) 5 ( 26.3) 0.870
ClC = 1 (%) 1 ( 4.2) 2 ( 10.5) 0.833
ClA = 0 (%) 24 (100.0) 19 (100.0) NA
SI = 1 (%) 1 ( 4.2) 3 ( 15.8) 0.439
APP = 1 (%) 4 ( 16.7) 1 ( 5.3) 0.497
Loisaika_1ennenpors_2_porskars = 2 (%) 8 ( 33.3) 8 ( 42.1) 0.785
Uusiryh (%) 0.597
1 2 ( 8.3) 1 ( 5.3)
2 20 ( 83.3) 18 ( 94.7)
3 1 ( 4.2) 0 ( 0.0)
4 1 ( 4.2) 0 ( 0.0)
Ton_tiheys_1aina_2jaetaan = 2 (%) 3 ( 12.5) 1 ( 5.3) 0.777
Yhdistaggrtmp_1eiongelma_2tmp_3eitmp (%) 0.591
1 4 ( 16.7) 1 ( 5.3)
2 11 ( 45.8) 11 ( 57.9)
3 4 ( 16.7) 2 ( 10.5)
12 5 ( 20.8) 5 ( 26.3)
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) 0.089
0 11 ( 45.8) 5 ( 26.3)
1 12 ( 50.0) 9 ( 47.4)
2 1 ( 4.2) 5 ( 26.3)
maitokuume = 1 (%) 12 ( 50.0) 10 ( 52.6) 1.000
metriitti = 1 (%) 10 ( 41.7) 9 ( 47.4) 0.948
valuttelu = 1 (%) 3 ( 12.5) 2 ( 10.5) 1.000
mastiitti = 1 (%) 5 ( 20.8) 5 ( 26.3) 0.953
ontuma = 1 (%) 15 ( 62.5) 16 ( 84.2) 0.217
syomattomyys = 1 (%) 14 ( 58.3) 8 ( 42.1) 0.453
kuume = 1 (%) 5 ( 20.8) 1 ( 5.3) 0.308
loukkaantuminen = 1 (%) 11 ( 45.8) 5 ( 26.3) 0.319
AB_rutiinilaak = 1 (%) 3 ( 12.5) 3 ( 15.8) 1.000
Oksitosiini_rutiinisti = 1 (%) 7 ( 29.2) 10 ( 52.6) 0.212
Kaynnistys_rutiinisti = 1 (%) 0 ( 0.0) 4 ( 21.1) 0.067
NSAID_porsituksessa_rutiini = 1 (%) 6 ( 25.0) 4 ( 21.1) 1.000
OMATENSIKOT_0EI_1KYLLa = 1 (%) 15 ( 62.5) 13 ( 68.4) 0.934
Ensikk_valisiirtkars_ennensiem = 1 (%) 8 ( 33.3) 9 ( 47.4) 0.535
Ensikk_kiihruok = 1 (%) 9 ( 37.5) 7 ( 36.8) 1.000
Ensikk_karjukontaktiensi_0hajutainako_1aidanlapi_2kars (%) 0.431
0 2 ( 8.3) 2 ( 10.5)
1 20 ( 83.3) 17 ( 89.5)
2 2 ( 8.3) 0 ( 0.0)
siemika (%) 0.161
7 1 ( 4.2) 0 ( 0.0)
7.5 0 ( 0.0) 2 ( 10.5)
8 20 ( 83.3) 14 ( 73.7)
8.5 1 ( 4.2) 3 ( 15.8)
9.5 2 ( 8.3) 0 ( 0.0)
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) 0.828
1 1 ( 4.2) 0 ( 0.0)
2 3 ( 12.5) 2 ( 10.5)
3 19 ( 79.2) 16 ( 84.2)
4 1 ( 4.2) 1 ( 5.3)
Kiimantark_ryhmakaytt = 1 (%) 21 ( 87.5) 17 ( 89.5) 1.000
Kiimantarkalkaa_vrkvier (%) 0.224
0 5 ( 20.8) 7 ( 36.8)
1 16 ( 66.7) 7 ( 36.8)
3 2 ( 8.3) 1 ( 5.3)
4 0 ( 0.0) 1 ( 5.3)
5 1 ( 4.2) 3 ( 15.8)
Kiimamerk_emakonselka = 1 (%) 18 ( 75.0) 19 (100.0) 0.057
Kiimantark_postsiem = 1 (%) 23 ( 95.8) 18 ( 94.7) 1.000
Postsiem_ryhmakaytt_havainnointi = 1 (%) 21 ( 87.5) 17 ( 89.5) 1.000
Tiin_ultra2 (%) 0.266
6 24 (100.0) 17 ( 89.5)
8 0 ( 0.0) 1 ( 5.3)
10 0 ( 0.0) 1 ( 5.3)
Tiin_ultra_1yhdesti_2kahdesti (%) 0.098
0 6 ( 25.0) 1 ( 5.3)
1 16 ( 66.7) 13 ( 68.4)
2 2 ( 8.3) 5 ( 26.3)
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) 0.085
0 10 ( 41.7) 6 ( 31.6)
1 0 ( 0.0) 2 ( 10.5)
2 2 ( 8.3) 6 ( 31.6)
3 2 ( 8.3) 0 ( 0.0)
4 10 ( 41.7) 5 ( 26.3)
Pesantekomatmaara_1runsas_2jnkv_3niukka (%) 0.269
1 3 ( 12.5) 0 ( 0.0)
2 17 ( 70.8) 16 ( 84.2)
3 4 ( 16.7) 3 ( 15.8)
Sisatutk_ennenoksitos = 1 (%) 10 ( 41.7) 5 ( 26.3) 0.467
Porsitusaputekn_1empesu_2kaspesu_3kasine_4liukaste (%) 0.532
34 10 ( 41.7) 5 ( 26.3)
124 1 ( 4.2) 1 ( 5.3)
134 7 ( 29.2) 10 ( 52.6)
234 1 ( 4.2) 0 ( 0.0)
1234 5 ( 20.8) 3 ( 15.8)
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) 20 ( 83.3) 15 ( 78.9) 1.000
Ruoksu_0ei_1itse_2neuvoja_3kyllaeitietoa (%) 0.445
1 1 ( 4.2) 1 ( 5.3)
2 21 ( 87.5) 13 ( 68.4)
3 1 ( 4.2) 2 ( 10.5)
12 1 ( 4.2) 3 ( 15.8)
Yksilöll_ruokinta = 1 (%) 16 ( 66.7) 15 ( 78.9) 0.583
AS_1ast_jout_samassa_2asteiole = 2 (%) 21 ( 87.5) 15 ( 78.9) 0.735
AS_er_os_lkm = 2 (%) 3 ( 12.5) 1 ( 5.3) 0.777
AS_em_kars (%) 0.391
2.5 1 ( 4.2) 0 ( 0.0)
7 0 ( 0.0) 1 ( 5.3)
7.5 22 ( 91.7) 17 ( 89.5)
8 0 ( 0.0) 1 ( 5.3)
60 1 ( 4.2) 0 ( 0.0)
AS_karspit (%) 0.448
3.31 1 ( 4.2) 0 ( 0.0)
4.4 1 ( 4.2) 0 ( 0.0)
5.94 21 ( 87.5) 18 ( 94.7)
7 0 ( 0.0) 1 ( 5.3)
20 1 ( 4.2) 0 ( 0.0)
AS_karslev (%) 0.448
2.67 1 ( 4.2) 0 ( 0.0)
3.02 0 ( 0.0) 1 ( 5.3)
4.8 21 ( 87.5) 18 ( 94.7)
6.8 1 ( 4.2) 0 ( 0.0)
7 1 ( 4.2) 0 ( 0.0)
AS_meluton = 1 (%) 21 ( 87.5) 18 ( 94.7) 0.777
AS_haittael_ei = 1 (%) 21 ( 87.5) 16 ( 84.2) 1.000
AS_haittael_laatu (%) 0.246
1 16 ( 66.7) 10 ( 52.6)
2 0 ( 0.0) 2 ( 10.5)
3 0 ( 0.0) 1 ( 5.3)
4 8 ( 33.3) 6 ( 31.6)
AS_ilma_aistin = 1 (%) 4 ( 16.7) 4 ( 21.1) 1.000
AS_ilma_amm = 1 (%) 4 ( 16.7) 4 ( 21.1) 1.000
AS_ilma_pöly = 0 (%) 24 (100.0) 19 (100.0) NA
AS_ilma_muu = 0 (%) 24 (100.0) 19 (100.0) NA
AS_kosteus = 0 (%) 24 (100.0) 19 (100.0) NA
AS_valaistus = 1 (%) 1 ( 4.2) 1 ( 5.3) 1.000
AS_alusta12345 = 12 (%) 3 ( 12.5) 4 ( 21.1) 0.735
AS_alusta_5_laatu = 0 (%) 24 (100.0) 19 (100.0) NA
AS_latt_rakenne1234 = 13 (%) 21 ( 87.5) 15 ( 78.9) 0.735
AS_pr_ritila (%) 0.594
0 21 ( 87.5) 15 ( 78.9)
20 1 ( 4.2) 2 ( 10.5)
25 1 ( 4.2) 1 ( 5.3)
33 0 ( 0.0) 1 ( 5.3)
41 1 ( 4.2) 0 ( 0.0)
AS_pr_viemar = 0 (%) 24 (100.0) 19 (100.0) NA
AS_kuiv_mat12345 (%) 0.315
1 3 ( 12.5) 1 ( 5.3)
1.5 19 ( 79.2) 16 ( 84.2)
2 0 ( 0.0) 2 ( 10.5)
12 1 ( 4.2) 0 ( 0.0)
14 1 ( 4.2) 0 ( 0.0)
AS_kuiv_5_mika (%) 0.513
0 1 ( 4.2) 1 ( 5.3)
3 23 ( 95.8) 17 ( 89.5)
4 0 ( 0.0) 1 ( 5.3)
AS_maara1234 (%) 0.633
0 1 ( 4.2) 0 ( 0.0)
1 1 ( 4.2) 0 ( 0.0)
2 1 ( 4.2) 0 ( 0.0)
3 2 ( 8.3) 2 ( 10.5)
4 19 ( 79.2) 17 ( 89.5)
AS_tonkimat123456 (%) 0.358
1 23 ( 95.8) 18 ( 94.7)
5 0 ( 0.0) 1 ( 5.3)
12 1 ( 4.2) 0 ( 0.0)
AS_tonkimat_6_mika = 0 (%) 24 (100.0) 19 (100.0) NA
AS_mat_vaiht = 1 (%) 22 ( 91.7) 19 (100.0) 0.576
AS_maara123 (%) 0.320
0 1 ( 4.2) 0 ( 0.0)
2 19 ( 79.2) 18 ( 94.7)
3 4 ( 16.7) 1 ( 5.3)
AS_annostelu1234 (%) 0.660
0 1 ( 4.2) 1 ( 5.3)
1 22 ( 91.7) 18 ( 94.7)
3 1 ( 4.2) 0 ( 0.0)
AS_lannanpoisto12 (%) 0.531
0 1 ( 4.2) 0 ( 0.0)
1 1 ( 4.2) 2 ( 10.5)
2 21 ( 87.5) 17 ( 89.5)
12 1 ( 4.2) 0 ( 0.0)
AS_rak_kunto = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
AS_latt_pitava = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
AS_sairkars = 1 (%) 6 ( 25.0) 5 ( 26.3) 1.000
AS_sk_parempi (%) 0.461
0 4 ( 16.7) 2 ( 10.5)
0.5 0 ( 0.0) 1 ( 5.3)
1 20 ( 83.3) 16 ( 84.2)
AS_sk_kiintea = 0 (%) 24 (100.0) 19 (100.0) NA
AS_sk_kuivike (%) 0.916
0 21 ( 87.5) 17 ( 89.5)
0.5 1 ( 4.2) 1 ( 5.3)
1 2 ( 8.3) 1 ( 5.3)
AS_sk_siisti = 1 (%) 2 ( 8.3) 1 ( 5.3) 1.000
AS_sk_kuiva (%) 0.358
0 23 ( 95.8) 18 ( 94.7)
0.5 0 ( 0.0) 1 ( 5.3)
1 1 ( 4.2) 0 ( 0.0)
AS_sk_syörauha = 1 (%) 1 ( 4.2) 4 ( 21.1) 0.216
AS_sk_juorauha = 1 (%) 1 ( 4.2) 4 ( 21.1) 0.216
AS_ruoklaite12345 = 4 (%) 24 (100.0) 17 ( 89.5) 0.369
AS_ruokpaikka (%) 0.266
0 0 ( 0.0) 1 ( 5.3)
1 24 (100.0) 17 ( 89.5)
4 0 ( 0.0) 1 ( 5.3)
AS_ruokpuht = 1 (%) 3 ( 12.5) 3 ( 15.8) 1.000
AS_juomalaite123 = 1 (%) 23 ( 95.8) 19 (100.0) 1.000
AS_juonalkm (%) 0.358
0.222222222222222 0 ( 0.0) 1 ( 5.3)
1 23 ( 95.8) 18 ( 94.7)
2.25 1 ( 4.2) 0 ( 0.0)
AS_juomapuht = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
AS_juomatoim = 0 (%) 24 (100.0) 19 (100.0) NA
AS_rauhallisuus123 = 1 (%) 22 ( 91.7) 19 (100.0) 0.576
AS_hoitotarveKE = 2 (%) 10 ( 41.7) 9 ( 47.4) 0.948
AS_stereo = 1 (%) 4 ( 16.7) 2 ( 10.5) 0.893
TII_1ast_jout_samassa_2asteiole (%) 0.953
0 21 ( 87.5) 16 ( 84.2)
1 2 ( 8.3) 2 ( 10.5)
2 1 ( 4.2) 1 ( 5.3)
TII_valiseinat (%) 0.491
0 22 ( 91.7) 16 ( 84.2)
0.5 0 ( 0.0) 1 ( 5.3)
1 1 ( 4.2) 1 ( 5.3)
2.5 1 ( 4.2) 0 ( 0.0)
16 0 ( 0.0) 1 ( 5.3)
TII_meluton = 1 (%) 17 ( 70.8) 17 ( 89.5) 0.265
TII_haittael_ei = 1 (%) 19 ( 79.2) 14 ( 73.7) 0.953
TII_ilma_aistin = 1 (%) 2 ( 8.3) 3 ( 15.8) 0.781
TII_ilma_amm = 1 (%) 3 ( 12.5) 3 ( 15.8) 1.000
TII_ilma_pöly = 0 (%) 24 (100.0) 19 (100.0) NA
TII_ilma_muu = 0 (%) 24 (100.0) 19 (100.0) NA
TII_kosteus = 0 (%) 24 (100.0) 19 (100.0) NA
TII_valaistus = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
TII_alusta12345 = 1 (%) 24 (100.0) 19 (100.0) NA
TII_latt_rakenne1234 (%) 0.471
1 4 ( 16.7) 1 ( 5.3)
12 2 ( 8.3) 2 ( 10.5)
13 18 ( 75.0) 15 ( 78.9)
23 0 ( 0.0) 1 ( 5.3)
TII_pr_ritila (%) 0.491
0 22 ( 91.7) 16 ( 84.2)
20 1 ( 4.2) 0 ( 0.0)
28 0 ( 0.0) 1 ( 5.3)
40 1 ( 4.2) 1 ( 5.3)
50 0 ( 0.0) 1 ( 5.3)
TII_pr_viemar = 0 (%) 24 (100.0) 19 (100.0) NA
TII_kuiv_mat12345 (%) 0.565
1 4 ( 16.7) 1 ( 5.3)
2 15 ( 62.5) 16 ( 84.2)
12 2 ( 8.3) 1 ( 5.3)
14 2 ( 8.3) 1 ( 5.3)
15 1 ( 4.2) 0 ( 0.0)
TII_kuiv_5_mika = 2 (%) 1 ( 4.2) 0 ( 0.0) 1.000
TII_maara1234 (%) 0.508
1 3 ( 12.5) 1 ( 5.3)
2 4 ( 16.7) 1 ( 5.3)
3 13 ( 54.2) 12 ( 63.2)
4 4 ( 16.7) 4 ( 21.1)
23 0 ( 0.0) 1 ( 5.3)
TII_tonkimat_6_mika (%) 0.186
1 23 ( 95.8) 15 ( 78.9)
2 1 ( 4.2) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.3)
4 0 ( 0.0) 1 ( 5.3)
5 0 ( 0.0) 2 ( 10.5)
TII_lelu1234 (%) 0.296
2 2 ( 8.3) 0 ( 0.0)
4 21 ( 87.5) 18 ( 94.7)
5 1 ( 4.2) 0 ( 0.0)
24 0 ( 0.0) 1 ( 5.3)
TII_mat_vaiht = 1 (%) 24 (100.0) 18 ( 94.7) 0.906
TII_maara123 (%) 0.877
1 3 ( 12.5) 1 ( 5.3)
1.5 1 ( 4.2) 1 ( 5.3)
2 19 ( 79.2) 16 ( 84.2)
3 1 ( 4.2) 1 ( 5.3)
TII_annostelu1234 (%) 0.513
1 23 ( 95.8) 17 ( 89.5)
2 0 ( 0.0) 1 ( 5.3)
4 1 ( 4.2) 1 ( 5.3)
TII_lannanpoisto12 (%) 0.486
1 14 ( 58.3) 9 ( 47.4)
2 1 ( 4.2) 0 ( 0.0)
3 3 ( 12.5) 2 ( 10.5)
4 1 ( 4.2) 0 ( 0.0)
5 5 ( 20.8) 8 ( 42.1)
TII_rak_kunto = 1 (%) 1 ( 4.2) 1 ( 5.3) 1.000
TII_latt_pitava = 1 (%) 1 ( 4.2) 2 ( 10.5) 0.833
TII_sairkars = 1 (%) 23 ( 95.8) 16 ( 84.2) 0.439
TII_ruok_0nonlock_1lock = 1 (%) 12 ( 50.0) 5 ( 26.3) 0.206
TII_ruokpuht = 0 (%) 24 (100.0) 19 (100.0) NA
TII_juomalaite123 (%) 0.358
1 23 ( 95.8) 18 ( 94.7)
2 1 ( 4.2) 0 ( 0.0)
12 0 ( 0.0) 1 ( 5.3)
TII_juomapuht = 0 (%) 24 (100.0) 19 (100.0) NA
TII_juomatoim = 2 (%) 0 ( 0.0) 1 ( 5.3) 0.906
TII_rauhallisuus123 = 2 (%) 0 ( 0.0) 1 ( 5.3) 0.906
TII_hoitotarveKE = 2 (%) 12 ( 50.0) 10 ( 52.6) 1.000
TII_stereo = 1 (%) 2 ( 8.3) 2 ( 10.5) 1.000
POR_meluton = 1 (%) 21 ( 87.5) 12 ( 63.2) 0.130
POR_haittael_ei = 1 (%) 21 ( 87.5) 16 ( 84.2) 1.000
POR_haittael_laatu (%) 0.561
1 14 ( 58.3) 11 ( 57.9)
2 1 ( 4.2) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.3)
4 9 ( 37.5) 7 ( 36.8)
POR_ilma_aistin = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
POR_ilma_amm = 1 (%) 1 ( 4.2) 0 ( 0.0) 1.000
POR_ilma_pöly = 0 (%) 24 (100.0) 19 (100.0) NA
POR_ilma_muu = 0 (%) 24 (100.0) 19 (100.0) NA
POR_kosteus = 0 (%) 24 (100.0) 19 (100.0) NA
POR_valaistus (%) 0.660
0 22 ( 91.7) 18 ( 94.7)
0.5 1 ( 4.2) 0 ( 0.0)
1 1 ( 4.2) 1 ( 5.3)
POR_latt_rakenne1234 (%) 0.164
1 2 ( 8.3) 0 ( 0.0)
2 3 ( 12.5) 0 ( 0.0)
12 18 ( 75.0) 18 ( 94.7)
13 0 ( 0.0) 1 ( 5.3)
123 1 ( 4.2) 0 ( 0.0)
POR_pr_rako = 38 (%) 0 ( 0.0) 1 ( 5.3) 0.906
POR_maara1234 (%) 0.265
2 5 ( 20.8) 3 ( 15.8)
3 14 ( 58.3) 15 ( 78.9)
4 5 ( 20.8) 1 ( 5.3)
POR_tonkimat_6_mika (%) 0.081
1 22 ( 91.7) 14 ( 73.7)
2 0 ( 0.0) 1 ( 5.3)
3 1 ( 4.2) 0 ( 0.0)
4 0 ( 0.0) 4 ( 21.1)
5 1 ( 4.2) 0 ( 0.0)
POR_lelu1234 (%) 0.643
2 1 ( 4.2) 0 ( 0.0)
3 1 ( 4.2) 1 ( 5.3)
4 21 ( 87.5) 18 ( 94.7)
5 1 ( 4.2) 0 ( 0.0)
POR_lelukomm (%) 0.414
1 22 ( 91.7) 18 ( 94.7)
2 1 ( 4.2) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.3)
4 1 ( 4.2) 0 ( 0.0)
POR_mat_vaiht = 2 (%) 0 ( 0.0) 1 ( 5.3) 0.906
POR_maara123 (%) 0.433
1 2 ( 8.3) 0 ( 0.0)
2 21 ( 87.5) 18 ( 94.7)
3 1 ( 4.2) 1 ( 5.3)
POR_annostelu1234 (%) 0.386
1 20 ( 83.3) 17 ( 89.5)
2 2 ( 8.3) 1 ( 5.3)
3 2 ( 8.3) 0 ( 0.0)
4 0 ( 0.0) 1 ( 5.3)
POR_lannanpoisto12 = 2 (%) 21 ( 87.5) 18 ( 94.7) 0.777
POR_rak_kunto = 1 (%) 2 ( 8.3) 1 ( 5.3) 1.000
POR_latt_pitava = 1 (%) 3 ( 12.5) 0 ( 0.0) 0.320
POR_sairkars (%) 0.679
1 17 ( 70.8) 12 ( 63.2)
2 1 ( 4.2) 0 ( 0.0)
3 2 ( 8.3) 2 ( 10.5)
4 4 ( 16.7) 4 ( 21.1)
5 0 ( 0.0) 1 ( 5.3)
POR_ruoklaite12345 (%) 0.554
2 1 ( 4.2) 1 ( 5.3)
2.5 22 ( 91.7) 17 ( 89.5)
3 1 ( 4.2) 0 ( 0.0)
25 0 ( 0.0) 1 ( 5.3)
POR_ruokpaikka = 1 (%) 24 (100.0) 19 (100.0) NA
POR_ruokpuht = 1 (%) 2 ( 8.3) 1 ( 5.3) 1.000
POR_juomalaite123 = 13 (%) 0 ( 0.0) 1 ( 5.3) 0.906
POR_juonalkm = 1 (%) 24 (100.0) 19 (100.0) NA
POR_juomapuht = 0 (%) 24 (100.0) 19 (100.0) NA
POR_juomatoim = 1 (%) 0 ( 0.0) 1 ( 5.3) 0.906
POR_rauhallisuus123 = 1 (%) 24 (100.0) 19 (100.0) NA
Hajukarjut_per_emakko (%) 0.564
0 3 ( 12.5) 4 ( 21.1)
0.01 14 ( 58.3) 11 ( 57.9)
0.02 4 ( 16.7) 1 ( 5.3)
0.03 3 ( 12.5) 2 ( 10.5)
0.06 0 ( 0.0) 1 ( 5.3)
TII_VIRMaa_0_ei_1pellel_2pelvir_3niukuihiemnvir_4riirunkuiv (%) 0.148
0 1 ( 4.2) 3 ( 15.8)
1 0 ( 0.0) 2 ( 10.5)
2 4 ( 16.7) 4 ( 21.1)
3 5 ( 20.8) 5 ( 26.3)
4 14 ( 58.3) 5 ( 26.3)
TII_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.214
0 1 ( 4.2) 3 ( 15.8)
1 5 ( 20.8) 4 ( 21.1)
2 13 ( 54.2) 5 ( 26.3)
3 5 ( 20.8) 7 ( 36.8)
AS_VIRMaa_0ei_1pellel_2pelvir_3niukuihiemvir_4riirunkuiv (%) 0.052
0 5 ( 20.8) 0 ( 0.0)
1 1 ( 4.2) 6 ( 31.6)
2 9 ( 37.5) 8 ( 42.1)
3 6 ( 25.0) 3 ( 15.8)
4 3 ( 12.5) 2 ( 10.5)
AS_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.065
0 5 ( 20.8) 0 ( 0.0)
1 9 ( 37.5) 13 ( 68.4)
2 6 ( 25.0) 2 ( 10.5)
3 4 ( 16.7) 4 ( 21.1)
POR_VIRMaa_0_ei_1pellel_2pelvir_3niukui_4riikuiv (%) 0.320
0 2 ( 8.3) 1 ( 5.3)
1 0 ( 0.0) 3 ( 15.8)
2 6 ( 25.0) 3 ( 15.8)
3 9 ( 37.5) 8 ( 42.1)
4 7 ( 29.2) 4 ( 21.1)
POR_VIR_LELUKPL_0ei_1yksi_2kaksi_3kolme (%) 0.415
0 1 ( 4.2) 1 ( 5.3)
1 4 ( 16.7) 5 ( 26.3)
2 18 ( 75.0) 10 ( 52.6)
3 1 ( 4.2) 3 ( 15.8)
Koulmax_1peru_2ops_3a_4amk_5yl (%) 0.833
2 2 ( 8.3) 3 ( 15.8)
3 16 ( 66.7) 12 ( 63.2)
4 4 ( 16.7) 2 ( 10.5)
5 2 ( 8.3) 2 ( 10.5)
Stressi_1erpal_4jnkv (%) 0.395
1 5 ( 20.8) 1 ( 5.3)
2 3 ( 12.5) 5 ( 26.3)
3 8 ( 33.3) 6 ( 31.6)
4 8 ( 33.3) 7 ( 36.8)
EMKUOLLJAKO = 1 (%) 6 ( 25.0) 14 ( 73.7) 0.004
EMPOISJAKO = 1 (%) 0 ( 0.0) 19 (100.0) <0.001
EMENKUOLLJAKO = 1 (%) 5 ( 20.8) 13 ( 68.4) 0.005
EMENPOISJAKO = 1 (%) 1 ( 4.2) 15 ( 78.9) <0.001
NIVEL_01 = 2 (%) 9 ( 37.5) 9 ( 47.4) 0.734
PAISE_01 = 2 (%) 9 ( 37.5) 9 ( 47.4) 0.734
MAKUU01 = 2 (%) 9 ( 37.5) 9 ( 47.4) 0.734
KOKO_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
OSA_01 = 2 (%) 10 ( 41.7) 8 ( 42.1) 1.000
JOKUHYLK_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
PLEUR_01 = 1 (%) 6 ( 25.0) 6 ( 31.6) 0.892
PNEUM_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
SAIRKARS_AST_TII = 1 (%) 18 ( 75.0) 15 ( 78.9) 1.000
EMKUOL (mean (sd)) 0.25 (0.44) 0.74 (0.45) 0.001
EMPOIS (mean (sd)) 0.00 (0.00) 1.00 (0.00) <0.001

MCA

tilatkat<-tilat[,1:218]%>%mutate_all(as.factor)
tilatnum<-tilat[,219:233]%>%mutate_all(as.numeric)
tilat<-cbind(tilatkat,tilatnum)
res_mca = MCA(tilat, quanti.sup = c(219:233), graph = FALSE) 

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

fviz_mca_var(res_mca, choice = "mca.cor", 
            repel = TRUE, # Avoid text overlapping (slow)
            ggtheme = theme_minimal())

To visualize the percentage of inertia explained by each MCA dimension:

fviz_mca_var(res_mca, col.var = "contrib",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
             repel = TRUE, # avoid text overlapping (slow)
             ggtheme = theme_minimal()
             )


# load data
setwd("~/GitHub/tilataso")
library(readr)
tilapieni<-read.csv(file="tilapieni.csv", header=TRUE)

Kuvailevat

Valitsen muutaman jatkuvan muuttujan ja muutoin valitsen ne, joissa on alle 6 kategoriaa. Yhteenveto muuttujista:

tilapienikat<-tilapieni[1:76]%>%mutate_all(as.factor)
tilapieninum<-tilapieni[77:92]%>%mutate_all(as.numeric)
tilapieni<-cbind(tilapieninum,tilapienikat)

summaryKable(tilapieni) %>% 
  kable("html", align = "rrr", caption = "Data variable summary") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>%
scroll_box(height = "300px")
Data variable summary
Min 1st Q Median Mean 3rd Q Max
Karjut_astsiem 0.000 0.000 0.000 0.419 0.000 6.000
emakot 37.000 102.500 270.000 428.837 635.000 2100.000
ensikot 0.000 17.000 30.000 70.930 76.500 710.000
lihasiat 0.000 0.000 40.000 381.512 390.000 3000.000
karjut 1.000 2.000 2.000 2.907 4.000 7.000
kokemusave 3.000 11.000 16.500 17.802 25.000 40.000
kokemusmax 5.000 20.000 25.000 25.349 30.000 47.000
emakoitaper 10.000 60.000 100.000 103.765 137.915 350.000
nivelpros 0.000 1.540 2.100 2.695 3.440 13.330
paisepros 0.000 4.460 6.800 6.886 8.885 16.280
keuhtulpros 0.000 0.000 0.920 1.004 1.475 3.590
keuhkopros 0.000 0.965 1.700 7.626 10.625 36.360
kokopros 0.000 0.735 1.360 1.808 2.185 7.250
osapros 0.000 8.080 11.210 11.908 15.325 34.620
emkuol 0.000 5.540 8.700 9.840 13.900 26.760
empoisp 25.730 42.710 47.610 52.106 58.340 120.600
Haastrooli_1OmEiosall_2OmOsall_3Esimies Levels 1: 10 2: 28 3: 5 – –
Tuotsuunta Levels 1: 22 2: 21 – – –
Tautsu_012 Levels 0: 13 1: 13 2: 17 – –
PORSOSASTO_kertayt_0ei Levels 0: 25 1: 18 – – –
PORS_pesu_0ei Levels 0: 10 1: 33 – – –
PORS_desinf_0ei_1LIU_2KUIVA Levels 0: 9 1: 19 2: 10 12: 5 –
PORS_tyhjana_mi1vr_0ei Levels 0: 17 1: 26 – – –
Tuhoelmerkkeja_0kylla_1ei Levels 0: 33 1: 10 – – –
kissoja0on1ei Levels 0: 26 0.5: 2 1: 15 – –
Kotielain_sikalaan_0kylla_1ei Levels 0: 9 1: 34 – – –
Vesi_1kunn_0oma Levels 0: 16 1: 27 – – –
ClC Levels 0: 40 1: 3 – – –
ClA Levels 0: 43 – – – –
SI Levels 0: 39 1: 4 – – –
APP Levels 0: 38 1: 5 – – –
Loisaika_1ennenpors_2_porskars Levels 1: 27 2: 16 – – –
Ton_tiheys_1aina_2jaetaan Levels 1: 39 2: 4 – – –
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann Levels 0: 16 1: 21 2: 6 – –
maitokuume Levels 0: 21 1: 22 – – –
metriitti Levels 0: 24 1: 19 – – –
valuttelu Levels 0: 38 1: 5 – – –
mastiitti Levels 0: 33 1: 10 – – –
ontuma Levels 0: 12 1: 31 – – –
syomattomyys Levels 0: 21 1: 22 – – –
kuume Levels 0: 37 1: 6 – – –
loukkaantuminen Levels 0: 27 1: 16 – – –
AB_rutiinilaak Levels 0: 37 1: 6 – – –
Oksitosiini_rutiinisti Levels 0: 26 1: 17 – – –
Kaynnistys_rutiinisti Levels 0: 39 1: 4 – – –
NSAID_porsituksessa_rutiini Levels 0: 33 1: 10 – – –
OMATENSIKOT_0EI_1KYLLa Levels 0: 15 1: 28 – – –
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk Levels 1: 1 2: 5 3: 35 4: 2 –
Kiimantark_ryhmakaytt Levels 0: 5 1: 38 – – –
Kiimantarkalkaa_vrkvier Levels 0: 12 1: 23 3: 3 4: 1 5: 4
Kiimamerk_emakonselka Levels 0: 6 1: 37 – – –
Kiimantark_postsiem Levels 0: 2 1: 41 – – –
Postsiem_ryhmakaytt_havainnointi Levels 0: 5 1: 38 – – –
Tiin_ultra2 Levels 6: 41 8: 1 10: 1 – –
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen Levels 0: 16 1: 2 2: 8 3: 2 4: 15
Pesantekomatmaara_1runsas_2jnkv_3niukka Levels 1: 3 2: 33 3: 7 – –
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa Levels 1: 8 2: 35 – – –
AS_maara123 Levels 0: 1 2: 37 3: 5 – –
AS_annostelu1234 Levels 0: 2 1: 40 3: 1 – –
AS_sairkars Levels 0: 32 1: 11 – – –
AS_ruoklaite12345 Levels 0: 2 4: 41 – – –
AS_ruokpaikka Levels 0: 1 1: 41 4: 1 – –
TII_alusta12345 Levels 1: 43 – – – –
TII_latt_rakenne1234 Levels 1: 5 12: 4 13: 33 23: 1 –
TII_kuiv_mat12345 Levels 1: 5 2: 31 12: 3 14: 3 15: 1
TII_maara1234 Levels 1: 4 2: 5 3: 25 4: 8 23: 1
TII_tonkimat_6_mika Levels 1: 38 2: 1 3: 1 4: 1 5: 2
TII_lelu1234 Levels 2: 2 4: 39 5: 1 24: 1 –
TII_maara123 Levels 1: 4 1.5: 2 2: 35 3: 2 –
TII_annostelu1234 Levels 1: 40 2: 1 4: 2 – –
TII_sairkars Levels 0: 4 1: 39 – – –
POR_latt_rakenne1234 Levels 1: 2 2: 3 12: 36 13: 1 123: 1
POR_maara1234 Levels 2: 8 3: 29 4: 6 – –
POR_tonkimat_6_mika Levels 1: 36 2: 1 3: 1 4: 4 5: 1
POR_lelu1234 Levels 2: 1 3: 2 4: 39 5: 1 –
POR_mat_vaiht Levels 1: 42 2: 1 – – –
POR_maara123 Levels 1: 2 2: 39 3: 2 – –
POR_annostelu1234 Levels 1: 37 2: 3 3: 2 4: 1 –
Koulmax_1peru_2ops_3a_4amk_5yl Levels 2: 5 3: 28 4: 6 5: 4 –
Stressi_1erpal_4jnkv Levels 1: 6 2: 8 3: 14 4: 15 –
EMKUOLLJAKO Levels 0: 23 1: 20 – – –
EMPOISJAKO Levels 0: 24 1: 19 – – –
EMENKUOLLJAKO Levels 0: 25 1: 18 – – –
EMENPOISJAKO Levels 0: 27 1: 16 – – –
NIVEL_01 Levels 1: 25 2: 18 – – –
MAKUU01 Levels 1: 25 2: 18 – – –
KOKO_01 Levels 1: 25 2: 18 – – –
OSA_01 Levels 1: 25 2: 18 – – –
JOKUHYLK_01 Levels 1: 25 2: 18 – – –
PLEUR_01 Levels 0: 31 1: 12 – – –
PNEUM_01 Levels 1: 25 2: 18 – – –
SAIRKARS_AST_TII Levels 0: 10 1: 33 – – –
kuvat2<-tilapienikat
colnames(kuvat2)
##  [1] "Haastrooli_1OmEiosall_2OmOsall_3Esimies"                 
##  [2] "Tuotsuunta"                                              
##  [3] "Tautsu_012"                                              
##  [4] "PORSOSASTO_kertayt_0ei"                                  
##  [5] "PORS_pesu_0ei"                                           
##  [6] "PORS_desinf_0ei_1LIU_2KUIVA"                             
##  [7] "PORS_tyhjana_mi1vr_0ei"                                  
##  [8] "Tuhoelmerkkeja_0kylla_1ei"                               
##  [9] "kissoja0on1ei"                                           
## [10] "Kotielain_sikalaan_0kylla_1ei"                           
## [11] "Vesi_1kunn_0oma"                                         
## [12] "ClC"                                                     
## [13] "ClA"                                                     
## [14] "SI"                                                      
## [15] "APP"                                                     
## [16] "Loisaika_1ennenpors_2_porskars"                          
## [17] "Ton_tiheys_1aina_2jaetaan"                               
## [18] "Muutelkaynn_0ei_1_satunn_2kaynnmuusaann"                 
## [19] "maitokuume"                                              
## [20] "metriitti"                                               
## [21] "valuttelu"                                               
## [22] "mastiitti"                                               
## [23] "ontuma"                                                  
## [24] "syomattomyys"                                            
## [25] "kuume"                                                   
## [26] "loukkaantuminen"                                         
## [27] "AB_rutiinilaak"                                          
## [28] "Oksitosiini_rutiinisti"                                  
## [29] "Kaynnistys_rutiinisti"                                   
## [30] "NSAID_porsituksessa_rutiini"                             
## [31] "OMATENSIKOT_0EI_1KYLLa"                                  
## [32] "Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk"   
## [33] "Kiimantark_ryhmakaytt"                                   
## [34] "Kiimantarkalkaa_vrkvier"                                 
## [35] "Kiimamerk_emakonselka"                                   
## [36] "Kiimantark_postsiem"                                     
## [37] "Postsiem_ryhmakaytt_havainnointi"                        
## [38] "Tiin_ultra2"                                             
## [39] "Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen"
## [40] "Pesantekomatmaara_1runsas_2jnkv_3niukka"                 
## [41] "PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa"             
## [42] "AS_maara123"                                             
## [43] "AS_annostelu1234"                                        
## [44] "AS_sairkars"                                             
## [45] "AS_ruoklaite12345"                                       
## [46] "AS_ruokpaikka"                                           
## [47] "TII_alusta12345"                                         
## [48] "TII_latt_rakenne1234"                                    
## [49] "TII_kuiv_mat12345"                                       
## [50] "TII_maara1234"                                           
## [51] "TII_tonkimat_6_mika"                                     
## [52] "TII_lelu1234"                                            
## [53] "TII_maara123"                                            
## [54] "TII_annostelu1234"                                       
## [55] "TII_sairkars"                                            
## [56] "POR_latt_rakenne1234"                                    
## [57] "POR_maara1234"                                           
## [58] "POR_tonkimat_6_mika"                                     
## [59] "POR_lelu1234"                                            
## [60] "POR_mat_vaiht"                                           
## [61] "POR_maara123"                                            
## [62] "POR_annostelu1234"                                       
## [63] "Koulmax_1peru_2ops_3a_4amk_5yl"                          
## [64] "Stressi_1erpal_4jnkv"                                    
## [65] "EMKUOLLJAKO"                                             
## [66] "EMPOISJAKO"                                              
## [67] "EMENKUOLLJAKO"                                           
## [68] "EMENPOISJAKO"                                            
## [69] "NIVEL_01"                                                
## [70] "MAKUU01"                                                 
## [71] "KOKO_01"                                                 
## [72] "OSA_01"                                                  
## [73] "JOKUHYLK_01"                                             
## [74] "PLEUR_01"                                                
## [75] "PNEUM_01"                                                
## [76] "SAIRKARS_AST_TII"
gather(kuvat2) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="purple") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

kor2<-tilapieninum


cor_fun <- function(data, mapping, method="pearson", ndp=2, sz=5, stars=TRUE, ...){

    data <- na.omit(data[,c(as.character(mapping$x), as.character(mapping$y))])

    x <- data[,as.character(mapping$x)]
    y <- data[,as.character(mapping$y)]

    corr <- cor.test(x, y, method=method)
    est <- corr$estimate
    lb.size <- sz* abs(est) 

    if(stars){
      stars <- c("***", "**", "*", "")[findInterval(corr$p.value, c(0, 0.001, 0.01, 0.05, 1))]
      lbl <- paste0(round(est, ndp), stars)
    }else{
      lbl <- round(est, ndp)
    }

    ggplot(data=data, mapping=mapping) + 
      annotate("text", x=mean(x), y=mean(y), label=lbl, size=lb.size,...)+
      theme(panel.grid = element_blank())
  }


ggpairs(kor2%>%mutate_all(as.numeric), 
        lower=list(continuous=wrap("smooth", colour="purple")),
        diag=list(continuous=wrap("barDiag", fill="purple")),
        upper=list(continuous=cor_fun),title="Graphical overview of the 17 variables")

KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table1 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='EMKUOLLJAKO')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by EMKUOL") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by EMKUOL
0 1 p test
n 23 20
Karjut_astsiem (mean (sd)) 0.13 (0.46) 0.75 (1.92) 0.140
emakot (mean (sd)) 305.26 (291.79) 570.95 (509.87) 0.039
ensikot (mean (sd)) 41.52 (45.16) 104.75 (174.17) 0.101
lihasiat (mean (sd)) 406.13 (718.12) 353.20 (773.30) 0.817
karjut (mean (sd)) 2.43 (1.59) 3.45 (1.90) 0.064
kokemusave (mean (sd)) 21.07 (10.17) 14.05 (7.17) 0.014
kokemusmax (mean (sd)) 27.39 (11.19) 23.00 (9.22) 0.172
emakoitaper (mean (sd)) 88.01 (46.20) 121.88 (74.77) 0.077
nivelpros (mean (sd)) 2.23 (2.37) 3.23 (2.68) 0.202
paisepros (mean (sd)) 6.45 (4.38) 7.39 (4.14) 0.478
keuhtulpros (mean (sd)) 0.87 (0.92) 1.16 (0.92) 0.315
keuhkopros (mean (sd)) 4.00 (7.16) 11.80 (13.39) 0.020
kokopros (mean (sd)) 1.35 (1.37) 2.34 (2.08) 0.070
osapros (mean (sd)) 11.59 (8.27) 12.28 (5.34) 0.752
emkuol (mean (sd)) 5.43 (2.52) 14.91 (4.55) <0.001
empoisp (mean (sd)) 44.45 (12.12) 60.90 (18.90) 0.001
Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) 0.431
1 4 ( 17.4) 6 ( 30.0)
2 17 ( 73.9) 11 ( 55.0)
3 2 ( 8.7) 3 ( 15.0)
Tuotsuunta = 2 (%) 14 ( 60.9) 7 ( 35.0) 0.165
Tautsu_012 (%) 0.733
0 8 ( 34.8) 5 ( 25.0)
1 7 ( 30.4) 6 ( 30.0)
2 8 ( 34.8) 9 ( 45.0)
PORSOSASTO_kertayt_0ei = 1 (%) 9 ( 39.1) 9 ( 45.0) 0.937
PORS_pesu_0ei = 1 (%) 17 ( 73.9) 16 ( 80.0) 0.913
PORS_desinf_0ei_1LIU_2KUIVA (%) 0.623
0 5 ( 21.7) 4 ( 20.0)
1 9 ( 39.1) 10 ( 50.0)
2 7 ( 30.4) 3 ( 15.0)
12 2 ( 8.7) 3 ( 15.0)
PORS_tyhjana_mi1vr_0ei = 1 (%) 13 ( 56.5) 13 ( 65.0) 0.799
Tuhoelmerkkeja_0kylla_1ei = 1 (%) 5 ( 21.7) 5 ( 25.0) 1.000
kissoja0on1ei (%) 0.005
0 19 ( 82.6) 7 ( 35.0)
0.5 0 ( 0.0) 2 ( 10.0)
1 4 ( 17.4) 11 ( 55.0)
Kotielain_sikalaan_0kylla_1ei = 1 (%) 17 ( 73.9) 17 ( 85.0) 0.606
Vesi_1kunn_0oma = 1 (%) 16 ( 69.6) 11 ( 55.0) 0.503
ClC = 1 (%) 1 ( 4.3) 2 ( 10.0) 0.900
ClA = 0 (%) 23 (100.0) 20 (100.0) NA
SI = 1 (%) 1 ( 4.3) 3 ( 15.0) 0.501
APP = 1 (%) 3 ( 13.0) 2 ( 10.0) 1.000
Loisaika_1ennenpors_2_porskars = 2 (%) 9 ( 39.1) 7 ( 35.0) 1.000
Ton_tiheys_1aina_2jaetaan = 2 (%) 4 ( 17.4) 0 ( 0.0) 0.152
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) 0.142
0 10 ( 43.5) 6 ( 30.0)
1 12 ( 52.2) 9 ( 45.0)
2 1 ( 4.3) 5 ( 25.0)
maitokuume = 1 (%) 12 ( 52.2) 10 ( 50.0) 1.000
metriitti = 1 (%) 10 ( 43.5) 9 ( 45.0) 1.000
valuttelu = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
mastiitti = 1 (%) 4 ( 17.4) 6 ( 30.0) 0.539
ontuma = 1 (%) 15 ( 65.2) 16 ( 80.0) 0.461
syomattomyys = 1 (%) 10 ( 43.5) 12 ( 60.0) 0.438
kuume = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
loukkaantuminen = 1 (%) 10 ( 43.5) 6 ( 30.0) 0.551
AB_rutiinilaak = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
Oksitosiini_rutiinisti = 1 (%) 8 ( 34.8) 9 ( 45.0) 0.711
Kaynnistys_rutiinisti = 1 (%) 0 ( 0.0) 4 ( 20.0) 0.084
NSAID_porsituksessa_rutiini = 1 (%) 6 ( 26.1) 4 ( 20.0) 0.913
OMATENSIKOT_0EI_1KYLLa = 1 (%) 15 ( 65.2) 13 ( 65.0) 1.000
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) 0.386
1 1 ( 4.3) 0 ( 0.0)
2 3 ( 13.0) 2 ( 10.0)
3 17 ( 73.9) 18 ( 90.0)
4 2 ( 8.7) 0 ( 0.0)
Kiimantark_ryhmakaytt = 1 (%) 20 ( 87.0) 18 ( 90.0) 1.000
Kiimantarkalkaa_vrkvier (%) 0.264
0 7 ( 30.4) 5 ( 25.0)
1 14 ( 60.9) 9 ( 45.0)
3 0 ( 0.0) 3 ( 15.0)
4 0 ( 0.0) 1 ( 5.0)
5 2 ( 8.7) 2 ( 10.0)
Kiimamerk_emakonselka = 1 (%) 17 ( 73.9) 20 (100.0) 0.043
Kiimantark_postsiem = 1 (%) 21 ( 91.3) 20 (100.0) 0.532
Postsiem_ryhmakaytt_havainnointi = 1 (%) 20 ( 87.0) 18 ( 90.0) 1.000
Tiin_ultra2 (%) 0.364
6 22 ( 95.7) 19 ( 95.0)
8 1 ( 4.3) 0 ( 0.0)
10 0 ( 0.0) 1 ( 5.0)
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) 0.115
0 10 ( 43.5) 6 ( 30.0)
1 0 ( 0.0) 2 ( 10.0)
2 2 ( 8.7) 6 ( 30.0)
3 2 ( 8.7) 0 ( 0.0)
4 9 ( 39.1) 6 ( 30.0)
Pesantekomatmaara_1runsas_2jnkv_3niukka (%) 0.763
1 1 ( 4.3) 2 ( 10.0)
2 18 ( 78.3) 15 ( 75.0)
3 4 ( 17.4) 3 ( 15.0)
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) 19 ( 82.6) 16 ( 80.0) 1.000
AS_maara123 (%) 0.054
0 0 ( 0.0) 1 ( 5.0)
2 18 ( 78.3) 19 ( 95.0)
3 5 ( 21.7) 0 ( 0.0)
AS_annostelu1234 (%) 0.639
0 1 ( 4.3) 1 ( 5.0)
1 21 ( 91.3) 19 ( 95.0)
3 1 ( 4.3) 0 ( 0.0)
AS_sairkars = 1 (%) 3 ( 13.0) 8 ( 40.0) 0.095
AS_ruoklaite12345 = 4 (%) 22 ( 95.7) 19 ( 95.0) 1.000
AS_ruokpaikka (%) 0.402
0 1 ( 4.3) 0 ( 0.0)
1 21 ( 91.3) 20 (100.0)
4 1 ( 4.3) 0 ( 0.0)
TII_alusta12345 = 1 (%) 23 (100.0) 20 (100.0) NA
TII_latt_rakenne1234 (%) 0.624
1 2 ( 8.7) 3 ( 15.0)
12 2 ( 8.7) 2 ( 10.0)
13 19 ( 82.6) 14 ( 70.0)
23 0 ( 0.0) 1 ( 5.0)
TII_kuiv_mat12345 (%) 0.508
1 4 ( 17.4) 1 ( 5.0)
2 15 ( 65.2) 16 ( 80.0)
12 1 ( 4.3) 2 ( 10.0)
14 2 ( 8.7) 1 ( 5.0)
15 1 ( 4.3) 0 ( 0.0)
TII_maara1234 (%) 0.669
1 3 ( 13.0) 1 ( 5.0)
2 2 ( 8.7) 3 ( 15.0)
3 14 ( 60.9) 11 ( 55.0)
4 4 ( 17.4) 4 ( 20.0)
23 0 ( 0.0) 1 ( 5.0)
TII_tonkimat_6_mika (%) 0.440
1 22 ( 95.7) 16 ( 80.0)
2 0 ( 0.0) 1 ( 5.0)
3 0 ( 0.0) 1 ( 5.0)
4 0 ( 0.0) 1 ( 5.0)
5 1 ( 4.3) 1 ( 5.0)
TII_lelu1234 (%) 0.257
2 2 ( 8.7) 0 ( 0.0)
4 21 ( 91.3) 18 ( 90.0)
5 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
TII_maara123 (%) 0.844
1 3 ( 13.0) 1 ( 5.0)
1.5 1 ( 4.3) 1 ( 5.0)
2 18 ( 78.3) 17 ( 85.0)
3 1 ( 4.3) 1 ( 5.0)
TII_annostelu1234 (%) 0.550
1 22 ( 95.7) 18 ( 90.0)
2 0 ( 0.0) 1 ( 5.0)
4 1 ( 4.3) 1 ( 5.0)
TII_sairkars = 1 (%) 21 ( 91.3) 18 ( 90.0) 1.000
POR_latt_rakenne1234 (%) 0.387
1 2 ( 8.7) 0 ( 0.0)
2 2 ( 8.7) 1 ( 5.0)
12 18 ( 78.3) 18 ( 90.0)
13 0 ( 0.0) 1 ( 5.0)
123 1 ( 4.3) 0 ( 0.0)
POR_maara1234 (%) 0.020
2 1 ( 4.3) 7 ( 35.0)
3 17 ( 73.9) 12 ( 60.0)
4 5 ( 21.7) 1 ( 5.0)
POR_tonkimat_6_mika (%) 0.307
1 21 ( 91.3) 15 ( 75.0)
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
4 1 ( 4.3) 3 ( 15.0)
5 0 ( 0.0) 1 ( 5.0)
POR_lelu1234 (%) 0.216
2 1 ( 4.3) 0 ( 0.0)
3 0 ( 0.0) 2 ( 10.0)
4 22 ( 95.7) 17 ( 85.0)
5 0 ( 0.0) 1 ( 5.0)
POR_mat_vaiht = 2 (%) 1 ( 4.3) 0 ( 0.0) 1.000
POR_maara123 (%) 0.401
1 2 ( 8.7) 0 ( 0.0)
2 20 ( 87.0) 19 ( 95.0)
3 1 ( 4.3) 1 ( 5.0)
POR_annostelu1234 (%) 0.763
1 19 ( 82.6) 18 ( 90.0)
2 2 ( 8.7) 1 ( 5.0)
3 1 ( 4.3) 1 ( 5.0)
4 1 ( 4.3) 0 ( 0.0)
Koulmax_1peru_2ops_3a_4amk_5yl (%) 0.613
2 3 ( 13.0) 2 ( 10.0)
3 15 ( 65.2) 13 ( 65.0)
4 4 ( 17.4) 2 ( 10.0)
5 1 ( 4.3) 3 ( 15.0)
Stressi_1erpal_4jnkv (%) 0.846
1 4 ( 17.4) 2 ( 10.0)
2 4 ( 17.4) 4 ( 20.0)
3 8 ( 34.8) 6 ( 30.0)
4 7 ( 30.4) 8 ( 40.0)
EMKUOLLJAKO = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
EMPOISJAKO = 1 (%) 5 ( 21.7) 14 ( 70.0) 0.004
EMENKUOLLJAKO = 1 (%) 1 ( 4.3) 17 ( 85.0) <0.001
EMENPOISJAKO = 1 (%) 3 ( 13.0) 13 ( 65.0) 0.001
NIVEL_01 = 2 (%) 8 ( 34.8) 10 ( 50.0) 0.485
MAKUU01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
KOKO_01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
OSA_01 = 2 (%) 9 ( 39.1) 9 ( 45.0) 0.937
JOKUHYLK_01 = 2 (%) 6 ( 26.1) 12 ( 60.0) 0.053
PLEUR_01 = 1 (%) 4 ( 17.4) 8 ( 40.0) 0.191
PNEUM_01 = 2 (%) 7 ( 30.4) 11 ( 55.0) 0.187
SAIRKARS_AST_TII = 1 (%) 15 ( 65.2) 18 ( 90.0) 0.120

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='EMPOISJAKO')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by EMPOIS") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by EMPOIS
0 1 p test
n 24 19
Karjut_astsiem (mean (sd)) 0.12 (0.45) 0.79 (1.96) 0.114
emakot (mean (sd)) 295.88 (277.03) 596.79 (518.67) 0.019
ensikot (mean (sd)) 37.08 (42.04) 113.68 (176.56) 0.046
lihasiat (mean (sd)) 287.54 (535.14) 500.21 (933.06) 0.353
karjut (mean (sd)) 2.25 (1.42) 3.74 (1.91) 0.006
kokemusave (mean (sd)) 18.94 (9.43) 16.37 (9.60) 0.384
kokemusmax (mean (sd)) 25.42 (10.78) 25.26 (10.29) 0.962
emakoitaper (mean (sd)) 88.13 (43.78) 123.52 (77.49) 0.066
nivelpros (mean (sd)) 2.37 (2.27) 3.11 (2.85) 0.349
paisepros (mean (sd)) 6.74 (4.76) 7.06 (3.61) 0.809
keuhtulpros (mean (sd)) 0.76 (0.73) 1.31 (1.06) 0.051
keuhkopros (mean (sd)) 5.05 (8.11) 10.88 (13.57) 0.088
kokopros (mean (sd)) 1.62 (1.80) 2.04 (1.79) 0.449
osapros (mean (sd)) 11.72 (8.09) 12.14 (5.50) 0.849
emkuol (mean (sd)) 7.42 (3.73) 12.90 (6.90) 0.002
empoisp (mean (sd)) 41.22 (6.16) 65.85 (17.65) <0.001
Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) 0.311
1 4 ( 16.7) 6 ( 31.6)
2 18 ( 75.0) 10 ( 52.6)
3 2 ( 8.3) 3 ( 15.8)
Tuotsuunta = 2 (%) 13 ( 54.2) 8 ( 42.1) 0.632
Tautsu_012 (%) 0.473
0 9 ( 37.5) 4 ( 21.1)
1 7 ( 29.2) 6 ( 31.6)
2 8 ( 33.3) 9 ( 47.4)
PORSOSASTO_kertayt_0ei = 1 (%) 8 ( 33.3) 10 ( 52.6) 0.336
PORS_pesu_0ei = 1 (%) 20 ( 83.3) 13 ( 68.4) 0.432
PORS_desinf_0ei_1LIU_2KUIVA (%) 0.894
0 4 ( 16.7) 5 ( 26.3)
1 11 ( 45.8) 8 ( 42.1)
2 6 ( 25.0) 4 ( 21.1)
12 3 ( 12.5) 2 ( 10.5)
PORS_tyhjana_mi1vr_0ei = 1 (%) 14 ( 58.3) 12 ( 63.2) 0.994
Tuhoelmerkkeja_0kylla_1ei = 1 (%) 6 ( 25.0) 4 ( 21.1) 1.000
kissoja0on1ei (%) 0.051
0 18 ( 75.0) 8 ( 42.1)
0.5 0 ( 0.0) 2 ( 10.5)
1 6 ( 25.0) 9 ( 47.4)
Kotielain_sikalaan_0kylla_1ei = 1 (%) 20 ( 83.3) 14 ( 73.7) 0.693
Vesi_1kunn_0oma = 1 (%) 15 ( 62.5) 12 ( 63.2) 1.000
ClC = 1 (%) 1 ( 4.2) 2 ( 10.5) 0.833
ClA = 0 (%) 24 (100.0) 19 (100.0) NA
SI = 1 (%) 1 ( 4.2) 3 ( 15.8) 0.439
APP = 1 (%) 4 ( 16.7) 1 ( 5.3) 0.497
Loisaika_1ennenpors_2_porskars = 2 (%) 8 ( 33.3) 8 ( 42.1) 0.785
Ton_tiheys_1aina_2jaetaan = 2 (%) 3 ( 12.5) 1 ( 5.3) 0.777
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) 0.089
0 11 ( 45.8) 5 ( 26.3)
1 12 ( 50.0) 9 ( 47.4)
2 1 ( 4.2) 5 ( 26.3)
maitokuume = 1 (%) 12 ( 50.0) 10 ( 52.6) 1.000
metriitti = 1 (%) 10 ( 41.7) 9 ( 47.4) 0.948
valuttelu = 1 (%) 3 ( 12.5) 2 ( 10.5) 1.000
mastiitti = 1 (%) 5 ( 20.8) 5 ( 26.3) 0.953
ontuma = 1 (%) 15 ( 62.5) 16 ( 84.2) 0.217
syomattomyys = 1 (%) 14 ( 58.3) 8 ( 42.1) 0.453
kuume = 1 (%) 5 ( 20.8) 1 ( 5.3) 0.308
loukkaantuminen = 1 (%) 11 ( 45.8) 5 ( 26.3) 0.319
AB_rutiinilaak = 1 (%) 3 ( 12.5) 3 ( 15.8) 1.000
Oksitosiini_rutiinisti = 1 (%) 7 ( 29.2) 10 ( 52.6) 0.212
Kaynnistys_rutiinisti = 1 (%) 0 ( 0.0) 4 ( 21.1) 0.067
NSAID_porsituksessa_rutiini = 1 (%) 6 ( 25.0) 4 ( 21.1) 1.000
OMATENSIKOT_0EI_1KYLLa = 1 (%) 15 ( 62.5) 13 ( 68.4) 0.934
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) 0.828
1 1 ( 4.2) 0 ( 0.0)
2 3 ( 12.5) 2 ( 10.5)
3 19 ( 79.2) 16 ( 84.2)
4 1 ( 4.2) 1 ( 5.3)
Kiimantark_ryhmakaytt = 1 (%) 21 ( 87.5) 17 ( 89.5) 1.000
Kiimantarkalkaa_vrkvier (%) 0.224
0 5 ( 20.8) 7 ( 36.8)
1 16 ( 66.7) 7 ( 36.8)
3 2 ( 8.3) 1 ( 5.3)
4 0 ( 0.0) 1 ( 5.3)
5 1 ( 4.2) 3 ( 15.8)
Kiimamerk_emakonselka = 1 (%) 18 ( 75.0) 19 (100.0) 0.057
Kiimantark_postsiem = 1 (%) 23 ( 95.8) 18 ( 94.7) 1.000
Postsiem_ryhmakaytt_havainnointi = 1 (%) 21 ( 87.5) 17 ( 89.5) 1.000
Tiin_ultra2 (%) 0.266
6 24 (100.0) 17 ( 89.5)
8 0 ( 0.0) 1 ( 5.3)
10 0 ( 0.0) 1 ( 5.3)
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) 0.085
0 10 ( 41.7) 6 ( 31.6)
1 0 ( 0.0) 2 ( 10.5)
2 2 ( 8.3) 6 ( 31.6)
3 2 ( 8.3) 0 ( 0.0)
4 10 ( 41.7) 5 ( 26.3)
Pesantekomatmaara_1runsas_2jnkv_3niukka (%) 0.269
1 3 ( 12.5) 0 ( 0.0)
2 17 ( 70.8) 16 ( 84.2)
3 4 ( 16.7) 3 ( 15.8)
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) 20 ( 83.3) 15 ( 78.9) 1.000
AS_maara123 (%) 0.320
0 1 ( 4.2) 0 ( 0.0)
2 19 ( 79.2) 18 ( 94.7)
3 4 ( 16.7) 1 ( 5.3)
AS_annostelu1234 (%) 0.660
0 1 ( 4.2) 1 ( 5.3)
1 22 ( 91.7) 18 ( 94.7)
3 1 ( 4.2) 0 ( 0.0)
AS_sairkars = 1 (%) 6 ( 25.0) 5 ( 26.3) 1.000
AS_ruoklaite12345 = 4 (%) 24 (100.0) 17 ( 89.5) 0.369
AS_ruokpaikka (%) 0.266
0 0 ( 0.0) 1 ( 5.3)
1 24 (100.0) 17 ( 89.5)
4 0 ( 0.0) 1 ( 5.3)
TII_alusta12345 = 1 (%) 24 (100.0) 19 (100.0) NA
TII_latt_rakenne1234 (%) 0.471
1 4 ( 16.7) 1 ( 5.3)
12 2 ( 8.3) 2 ( 10.5)
13 18 ( 75.0) 15 ( 78.9)
23 0 ( 0.0) 1 ( 5.3)
TII_kuiv_mat12345 (%) 0.565
1 4 ( 16.7) 1 ( 5.3)
2 15 ( 62.5) 16 ( 84.2)
12 2 ( 8.3) 1 ( 5.3)
14 2 ( 8.3) 1 ( 5.3)
15 1 ( 4.2) 0 ( 0.0)
TII_maara1234 (%) 0.508
1 3 ( 12.5) 1 ( 5.3)
2 4 ( 16.7) 1 ( 5.3)
3 13 ( 54.2) 12 ( 63.2)
4 4 ( 16.7) 4 ( 21.1)
23 0 ( 0.0) 1 ( 5.3)
TII_tonkimat_6_mika (%) 0.186
1 23 ( 95.8) 15 ( 78.9)
2 1 ( 4.2) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.3)
4 0 ( 0.0) 1 ( 5.3)
5 0 ( 0.0) 2 ( 10.5)
TII_lelu1234 (%) 0.296
2 2 ( 8.3) 0 ( 0.0)
4 21 ( 87.5) 18 ( 94.7)
5 1 ( 4.2) 0 ( 0.0)
24 0 ( 0.0) 1 ( 5.3)
TII_maara123 (%) 0.877
1 3 ( 12.5) 1 ( 5.3)
1.5 1 ( 4.2) 1 ( 5.3)
2 19 ( 79.2) 16 ( 84.2)
3 1 ( 4.2) 1 ( 5.3)
TII_annostelu1234 (%) 0.513
1 23 ( 95.8) 17 ( 89.5)
2 0 ( 0.0) 1 ( 5.3)
4 1 ( 4.2) 1 ( 5.3)
TII_sairkars = 1 (%) 23 ( 95.8) 16 ( 84.2) 0.439
POR_latt_rakenne1234 (%) 0.164
1 2 ( 8.3) 0 ( 0.0)
2 3 ( 12.5) 0 ( 0.0)
12 18 ( 75.0) 18 ( 94.7)
13 0 ( 0.0) 1 ( 5.3)
123 1 ( 4.2) 0 ( 0.0)
POR_maara1234 (%) 0.265
2 5 ( 20.8) 3 ( 15.8)
3 14 ( 58.3) 15 ( 78.9)
4 5 ( 20.8) 1 ( 5.3)
POR_tonkimat_6_mika (%) 0.081
1 22 ( 91.7) 14 ( 73.7)
2 0 ( 0.0) 1 ( 5.3)
3 1 ( 4.2) 0 ( 0.0)
4 0 ( 0.0) 4 ( 21.1)
5 1 ( 4.2) 0 ( 0.0)
POR_lelu1234 (%) 0.643
2 1 ( 4.2) 0 ( 0.0)
3 1 ( 4.2) 1 ( 5.3)
4 21 ( 87.5) 18 ( 94.7)
5 1 ( 4.2) 0 ( 0.0)
POR_mat_vaiht = 2 (%) 0 ( 0.0) 1 ( 5.3) 0.906
POR_maara123 (%) 0.433
1 2 ( 8.3) 0 ( 0.0)
2 21 ( 87.5) 18 ( 94.7)
3 1 ( 4.2) 1 ( 5.3)
POR_annostelu1234 (%) 0.386
1 20 ( 83.3) 17 ( 89.5)
2 2 ( 8.3) 1 ( 5.3)
3 2 ( 8.3) 0 ( 0.0)
4 0 ( 0.0) 1 ( 5.3)
Koulmax_1peru_2ops_3a_4amk_5yl (%) 0.833
2 2 ( 8.3) 3 ( 15.8)
3 16 ( 66.7) 12 ( 63.2)
4 4 ( 16.7) 2 ( 10.5)
5 2 ( 8.3) 2 ( 10.5)
Stressi_1erpal_4jnkv (%) 0.395
1 5 ( 20.8) 1 ( 5.3)
2 3 ( 12.5) 5 ( 26.3)
3 8 ( 33.3) 6 ( 31.6)
4 8 ( 33.3) 7 ( 36.8)
EMKUOLLJAKO = 1 (%) 6 ( 25.0) 14 ( 73.7) 0.004
EMPOISJAKO = 1 (%) 0 ( 0.0) 19 (100.0) <0.001
EMENKUOLLJAKO = 1 (%) 5 ( 20.8) 13 ( 68.4) 0.005
EMENPOISJAKO = 1 (%) 1 ( 4.2) 15 ( 78.9) <0.001
NIVEL_01 = 2 (%) 9 ( 37.5) 9 ( 47.4) 0.734
MAKUU01 = 2 (%) 9 ( 37.5) 9 ( 47.4) 0.734
KOKO_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
OSA_01 = 2 (%) 10 ( 41.7) 8 ( 42.1) 1.000
JOKUHYLK_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
PLEUR_01 = 1 (%) 6 ( 25.0) 6 ( 31.6) 0.892
PNEUM_01 = 2 (%) 8 ( 33.3) 10 ( 52.6) 0.336
SAIRKARS_AST_TII = 1 (%) 18 ( 75.0) 15 ( 78.9) 1.000

Yhteenveto joku hylkays mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table3 = KreateTableOne(x=tilapieni, factorVars=colnames(tilapienikat), strata='JOKUHYLK_01')
table3%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by JOKUHYLK") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by JOKUHYLK
1 2 p test
n 25 18
Karjut_astsiem (mean (sd)) 0.36 (1.25) 0.50 (1.54) 0.745
emakot (mean (sd)) 323.76 (312.17) 574.78 (518.46) 0.055
ensikot (mean (sd)) 43.88 (48.42) 108.50 (182.14) 0.097
lihasiat (mean (sd)) 256.44 (609.78) 555.22 (870.39) 0.192
karjut (mean (sd)) 2.60 (1.53) 3.33 (2.09) 0.190
kokemusave (mean (sd)) 19.30 (8.91) 15.72 (10.11) 0.227
kokemusmax (mean (sd)) 26.80 (10.61) 23.33 (10.14) 0.288
emakoitaper (mean (sd)) 88.71 (50.44) 124.67 (73.11) 0.063
nivelpros (mean (sd)) 1.98 (2.24) 3.69 (2.66) 0.027
paisepros (mean (sd)) 4.82 (2.72) 9.75 (4.37) <0.001
keuhtulpros (mean (sd)) 0.77 (0.82) 1.33 (0.97) 0.046
keuhkopros (mean (sd)) 1.60 (2.23) 15.99 (13.07) <0.001
kokopros (mean (sd)) 0.95 (0.81) 2.99 (2.10) <0.001
osapros (mean (sd)) 8.60 (4.88) 16.51 (6.96) <0.001
emkuol (mean (sd)) 7.85 (4.73) 12.60 (6.51) 0.008
empoisp (mean (sd)) 50.27 (19.07) 54.66 (15.30) 0.425
Haastrooli_1OmEiosall_2OmOsall_3Esimies (%) 0.539
1 6 ( 24.0) 4 ( 22.2)
2 15 ( 60.0) 13 ( 72.2)
3 4 ( 16.0) 1 ( 5.6)
Tuotsuunta = 2 (%) 12 ( 48.0) 9 ( 50.0) 1.000
Tautsu_012 (%) 0.922
0 8 ( 32.0) 5 ( 27.8)
1 7 ( 28.0) 6 ( 33.3)
2 10 ( 40.0) 7 ( 38.9)
PORSOSASTO_kertayt_0ei = 1 (%) 8 ( 32.0) 10 ( 55.6) 0.218
PORS_pesu_0ei = 1 (%) 19 ( 76.0) 14 ( 77.8) 1.000
PORS_desinf_0ei_1LIU_2KUIVA (%) 0.913
0 6 ( 24.0) 3 ( 16.7)
1 10 ( 40.0) 9 ( 50.0)
2 6 ( 24.0) 4 ( 22.2)
12 3 ( 12.0) 2 ( 11.1)
PORS_tyhjana_mi1vr_0ei = 1 (%) 14 ( 56.0) 12 ( 66.7) 0.697
Tuhoelmerkkeja_0kylla_1ei = 1 (%) 7 ( 28.0) 3 ( 16.7) 0.616
kissoja0on1ei (%) 0.962
0 15 ( 60.0) 11 ( 61.1)
0.5 1 ( 4.0) 1 ( 5.6)
1 9 ( 36.0) 6 ( 33.3)
Kotielain_sikalaan_0kylla_1ei = 1 (%) 21 ( 84.0) 13 ( 72.2) 0.578
Vesi_1kunn_0oma = 1 (%) 18 ( 72.0) 9 ( 50.0) 0.249
ClC = 1 (%) 1 ( 4.0) 2 ( 11.1) 0.767
ClA = 0 (%) 25 (100.0) 18 (100.0) NA
SI = 1 (%) 0 ( 0.0) 4 ( 22.2) 0.052
APP = 1 (%) 4 ( 16.0) 1 ( 5.6) 0.567
Loisaika_1ennenpors_2_porskars = 2 (%) 10 ( 40.0) 6 ( 33.3) 0.899
Ton_tiheys_1aina_2jaetaan = 2 (%) 3 ( 12.0) 1 ( 5.6) 0.853
Muutelkaynn_0ei_1_satunn_2kaynnmuusaann (%) 0.082
0 10 ( 40.0) 6 ( 33.3)
1 14 ( 56.0) 7 ( 38.9)
2 1 ( 4.0) 5 ( 27.8)
maitokuume = 1 (%) 13 ( 52.0) 9 ( 50.0) 1.000
metriitti = 1 (%) 9 ( 36.0) 10 ( 55.6) 0.336
valuttelu = 1 (%) 1 ( 4.0) 4 ( 22.2) 0.175
mastiitti = 1 (%) 6 ( 24.0) 4 ( 22.2) 1.000
ontuma = 1 (%) 18 ( 72.0) 13 ( 72.2) 1.000
syomattomyys = 1 (%) 12 ( 48.0) 10 ( 55.6) 0.857
kuume = 1 (%) 2 ( 8.0) 4 ( 22.2) 0.378
loukkaantuminen = 1 (%) 10 ( 40.0) 6 ( 33.3) 0.899
AB_rutiinilaak = 1 (%) 2 ( 8.0) 4 ( 22.2) 0.378
Oksitosiini_rutiinisti = 1 (%) 7 ( 28.0) 10 ( 55.6) 0.132
Kaynnistys_rutiinisti = 1 (%) 0 ( 0.0) 4 ( 22.2) 0.052
NSAID_porsituksessa_rutiini = 1 (%) 7 ( 28.0) 3 ( 16.7) 0.616
OMATENSIKOT_0EI_1KYLLa = 1 (%) 19 ( 76.0) 9 ( 50.0) 0.150
Ensikk_yhdist_1ennsiem_2tiineena_3porsjalk_4tilantmuk (%) 0.415
1 1 ( 4.0) 0 ( 0.0)
2 2 ( 8.0) 3 ( 16.7)
3 20 ( 80.0) 15 ( 83.3)
4 2 ( 8.0) 0 ( 0.0)
Kiimantark_ryhmakaytt = 1 (%) 24 ( 96.0) 14 ( 77.8) 0.175
Kiimantarkalkaa_vrkvier (%) 0.242
0 5 ( 20.0) 7 ( 38.9)
1 15 ( 60.0) 8 ( 44.4)
3 3 ( 12.0) 0 ( 0.0)
4 0 ( 0.0) 1 ( 5.6)
5 2 ( 8.0) 2 ( 11.1)
Kiimamerk_emakonselka = 1 (%) 21 ( 84.0) 16 ( 88.9) 0.992
Kiimantark_postsiem = 1 (%) 24 ( 96.0) 17 ( 94.4) 1.000
Postsiem_ryhmakaytt_havainnointi = 1 (%) 21 ( 84.0) 17 ( 94.4) 0.567
Tiin_ultra2 (%) 0.348
6 24 ( 96.0) 17 ( 94.4)
8 1 ( 4.0) 0 ( 0.0)
10 0 ( 0.0) 1 ( 5.6)
Kaynnistaminen_0ei_1rutiini_2yliaika_3ryhma_4satunnainen (%) 0.130
0 12 ( 48.0) 4 ( 22.2)
1 0 ( 0.0) 2 ( 11.1)
2 4 ( 16.0) 4 ( 22.2)
3 2 ( 8.0) 0 ( 0.0)
4 7 ( 28.0) 8 ( 44.4)
Pesantekomatmaara_1runsas_2jnkv_3niukka (%) 0.242
1 3 ( 12.0) 0 ( 0.0)
2 19 ( 76.0) 14 ( 77.8)
3 3 ( 12.0) 4 ( 22.2)
PorsitusNSAID_0ei_1rutiinisti_2tarvittaessa = 2 (%) 20 ( 80.0) 15 ( 83.3) 1.000
AS_maara123 (%) 0.376
0 1 ( 4.0) 0 ( 0.0)
2 20 ( 80.0) 17 ( 94.4)
3 4 ( 16.0) 1 ( 5.6)
AS_annostelu1234 (%) 0.472
0 1 ( 4.0) 1 ( 5.6)
1 24 ( 96.0) 16 ( 88.9)
3 0 ( 0.0) 1 ( 5.6)
AS_sairkars = 1 (%) 6 ( 24.0) 5 ( 27.8) 1.000
AS_ruoklaite12345 = 4 (%) 24 ( 96.0) 17 ( 94.4) 1.000
AS_ruokpaikka (%) 0.470
0 1 ( 4.0) 0 ( 0.0)
1 23 ( 92.0) 18 (100.0)
4 1 ( 4.0) 0 ( 0.0)
TII_alusta12345 = 1 (%) 25 (100.0) 18 (100.0) NA
TII_latt_rakenne1234 (%) 0.662
1 3 ( 12.0) 2 ( 11.1)
12 2 ( 8.0) 2 ( 11.1)
13 20 ( 80.0) 13 ( 72.2)
23 0 ( 0.0) 1 ( 5.6)
TII_kuiv_mat12345 (%) 0.181
1 3 ( 12.0) 2 ( 11.1)
2 15 ( 60.0) 16 ( 88.9)
12 3 ( 12.0) 0 ( 0.0)
14 3 ( 12.0) 0 ( 0.0)
15 1 ( 4.0) 0 ( 0.0)
TII_maara1234 (%) 0.064
1 4 ( 16.0) 0 ( 0.0)
2 4 ( 16.0) 1 ( 5.6)
3 15 ( 60.0) 10 ( 55.6)
4 2 ( 8.0) 6 ( 33.3)
23 0 ( 0.0) 1 ( 5.6)
TII_tonkimat_6_mika (%) 0.577
1 22 ( 88.0) 16 ( 88.9)
2 1 ( 4.0) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.6)
4 1 ( 4.0) 0 ( 0.0)
5 1 ( 4.0) 1 ( 5.6)
TII_lelu1234 (%) 0.308
2 2 ( 8.0) 0 ( 0.0)
4 22 ( 88.0) 17 ( 94.4)
5 1 ( 4.0) 0 ( 0.0)
24 0 ( 0.0) 1 ( 5.6)
TII_maara123 (%) 0.037
1 4 ( 16.0) 0 ( 0.0)
1.5 0 ( 0.0) 2 ( 11.1)
2 21 ( 84.0) 14 ( 77.8)
3 0 ( 0.0) 2 ( 11.1)
TII_annostelu1234 (%) 0.313
1 22 ( 88.0) 18 (100.0)
2 1 ( 4.0) 0 ( 0.0)
4 2 ( 8.0) 0 ( 0.0)
TII_sairkars = 1 (%) 24 ( 96.0) 15 ( 83.3) 0.380
POR_latt_rakenne1234 (%) 0.443
1 2 ( 8.0) 0 ( 0.0)
2 2 ( 8.0) 1 ( 5.6)
12 20 ( 80.0) 16 ( 88.9)
13 0 ( 0.0) 1 ( 5.6)
123 1 ( 4.0) 0 ( 0.0)
POR_maara1234 (%) 0.448
2 6 ( 24.0) 2 ( 11.1)
3 15 ( 60.0) 14 ( 77.8)
4 4 ( 16.0) 2 ( 11.1)
POR_tonkimat_6_mika (%) 0.668
1 20 ( 80.0) 16 ( 88.9)
2 1 ( 4.0) 0 ( 0.0)
3 1 ( 4.0) 0 ( 0.0)
4 2 ( 8.0) 2 ( 11.1)
5 1 ( 4.0) 0 ( 0.0)
POR_lelu1234 (%) 0.537
2 0 ( 0.0) 1 ( 5.6)
3 1 ( 4.0) 1 ( 5.6)
4 23 ( 92.0) 16 ( 88.9)
5 1 ( 4.0) 0 ( 0.0)
POR_mat_vaiht = 2 (%) 1 ( 4.0) 0 ( 0.0) 1.000
POR_maara123 (%) 0.121
1 2 ( 8.0) 0 ( 0.0)
2 23 ( 92.0) 16 ( 88.9)
3 0 ( 0.0) 2 ( 11.1)
POR_annostelu1234 (%) 0.827
1 21 ( 84.0) 16 ( 88.9)
2 2 ( 8.0) 1 ( 5.6)
3 1 ( 4.0) 1 ( 5.6)
4 1 ( 4.0) 0 ( 0.0)
Koulmax_1peru_2ops_3a_4amk_5yl (%) 0.220
2 3 ( 12.0) 2 ( 11.1)
3 19 ( 76.0) 9 ( 50.0)
4 2 ( 8.0) 4 ( 22.2)
5 1 ( 4.0) 3 ( 16.7)
Stressi_1erpal_4jnkv (%) 0.527
1 4 ( 16.0) 2 ( 11.1)
2 4 ( 16.0) 4 ( 22.2)
3 10 ( 40.0) 4 ( 22.2)
4 7 ( 28.0) 8 ( 44.4)
EMKUOLLJAKO = 1 (%) 8 ( 32.0) 12 ( 66.7) 0.053
EMPOISJAKO = 1 (%) 9 ( 36.0) 10 ( 55.6) 0.336
EMENKUOLLJAKO = 1 (%) 7 ( 28.0) 11 ( 61.1) 0.063
EMENPOISJAKO = 1 (%) 6 ( 24.0) 10 ( 55.6) 0.073
NIVEL_01 = 2 (%) 5 ( 20.0) 13 ( 72.2) 0.002
MAKUU01 = 2 (%) 4 ( 16.0) 14 ( 77.8) <0.001
KOKO_01 = 2 (%) 4 ( 16.0) 14 ( 77.8) <0.001
OSA_01 = 2 (%) 4 ( 16.0) 14 ( 77.8) <0.001
JOKUHYLK_01 = 2 (%) 0 ( 0.0) 18 (100.0) <0.001
PLEUR_01 = 1 (%) 1 ( 4.0) 11 ( 61.1) <0.001
PNEUM_01 = 2 (%) 5 ( 20.0) 13 ( 72.2) 0.002
SAIRKARS_AST_TII = 1 (%) 19 ( 76.0) 14 ( 77.8) 1.000

MCA

res_mca = MCA(tilapieni, quanti.sup = c(1:16), graph = FALSE) 
summary(res_mca)
## 
## Call:
## MCA(X = tilapieni, quanti.sup = c(1:16), graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.125   0.094   0.089   0.087   0.078   0.074
## % of var.              7.586   5.695   5.398   5.287   4.767   4.503
## Cumulative % of var.   7.586  13.282  18.680  23.967  28.734  33.238
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.065   0.064   0.061   0.057   0.056   0.053
## % of var.              3.932   3.871   3.680   3.446   3.411   3.230
## Cumulative % of var.  37.169  41.040  44.720  48.166  51.576  54.806
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.052   0.047   0.047   0.046   0.042   0.041
## % of var.              3.155   2.875   2.834   2.782   2.528   2.472
## Cumulative % of var.  57.961  60.836  63.670  66.452  68.980  71.453
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.039   0.036   0.034   0.032   0.030   0.030
## % of var.              2.394   2.212   2.048   1.930   1.848   1.807
## Cumulative % of var.  73.847  76.059  78.107  80.036  81.885  83.692
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.027   0.025   0.023   0.022   0.018   0.018
## % of var.              1.658   1.542   1.386   1.351   1.115   1.092
## Cumulative % of var.  85.350  86.891  88.278  89.629  90.743  91.836
##                       Dim.31  Dim.32  Dim.33  Dim.34  Dim.35  Dim.36
## Variance               0.017   0.016   0.016   0.014   0.012   0.012
## % of var.              1.044   0.979   0.953   0.880   0.759   0.735
## Cumulative % of var.  92.880  93.859  94.811  95.691  96.451  97.186
##                       Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               0.011   0.009   0.008   0.007   0.006   0.005
## % of var.              0.657   0.523   0.476   0.430   0.395   0.332
## Cumulative % of var.  97.844  98.367  98.842  99.273  99.668 100.000
## 
## Individuals (the 10 first)
##                                              Dim.1    ctr   cos2    Dim.2
## 1                                         |  0.208  0.808  0.031 | -0.072
## 2                                         | -0.413  3.179  0.075 | -0.312
## 3                                         | -0.345  2.216  0.040 |  1.056
## 4                                         |  0.096  0.173  0.011 | -0.052
## 5                                         |  0.600  6.713  0.234 | -0.039
## 6                                         | -0.217  0.880  0.047 |  0.028
## 7                                         |  0.302  1.699  0.093 |  0.049
## 8                                         | -0.265  1.312  0.029 |  0.426
## 9                                         | -0.471  4.134  0.081 | -0.446
## 10                                        | -0.019  0.006  0.000 |  0.052
##                                              ctr   cos2    Dim.3    ctr
## 1                                          0.129  0.004 |  0.070  0.129
## 2                                          2.423  0.043 |  0.214  1.195
## 3                                         27.707  0.374 |  0.339  3.002
## 4                                          0.068  0.003 |  0.063  0.104
## 5                                          0.037  0.001 | -0.060  0.096
## 6                                          0.020  0.001 | -0.011  0.003
## 7                                          0.060  0.002 |  0.054  0.077
## 8                                          4.510  0.075 | -1.002 26.273
## 9                                          4.937  0.073 |  0.566  8.383
## 10                                         0.068  0.002 | -0.363  3.451
##                                             cos2  
## 1                                          0.004 |
## 2                                          0.020 |
## 3                                          0.038 |
## 4                                          0.005 |
## 5                                          0.002 |
## 6                                          0.000 |
## 7                                          0.003 |
## 8                                          0.415 |
## 9                                          0.117 |
## 10                                         0.099 |
## 
## Categories (the 10 first)
##                                              Dim.1    ctr   cos2 v.test  
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1 | -0.229  0.129  0.016 -0.817 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 |  0.013  0.001  0.000  0.111 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3 |  0.387  0.184  0.020  0.911 |
## Tuotsuunta_1                              |  0.166  0.149  0.029  1.101 |
## Tuotsuunta_2                              | -0.174  0.156  0.029 -1.101 |
## Tautsu_012_0                              | -0.324  0.335  0.046 -1.383 |
## Tautsu_012_1                              |  0.295  0.278  0.038  1.260 |
## Tautsu_012_2                              |  0.022  0.002  0.000  0.115 |
## PORSOSASTO_kertayt_0ei_0                  | -0.388  0.925  0.210 -2.967 |
## PORSOSASTO_kertayt_0ei_1                  |  0.540  1.285  0.210  2.967 |
##                                            Dim.2    ctr   cos2 v.test  
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1  0.435  0.617  0.057  1.551 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 -0.171  0.269  0.055 -1.518 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3  0.091  0.014  0.001  0.214 |
## Tuotsuunta_1                              -0.121  0.105  0.015 -0.803 |
## Tuotsuunta_2                               0.127  0.110  0.015  0.803 |
## Tautsu_012_0                               0.068  0.019  0.002  0.289 |
## Tautsu_012_1                              -0.190  0.153  0.016 -0.809 |
## Tautsu_012_2                               0.093  0.048  0.006  0.488 |
## PORSOSASTO_kertayt_0ei_0                   0.095  0.074  0.013  0.729 |
## PORSOSASTO_kertayt_0ei_1                  -0.133  0.103  0.013 -0.729 |
##                                            Dim.3    ctr   cos2 v.test  
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_1  0.569  1.115  0.098  2.029 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_2 -0.136  0.179  0.035 -1.206 |
## Haastrooli_1OmEiosall_2OmOsall_3Esimies_3 -0.375  0.242  0.018 -0.881 |
## Tuotsuunta_1                              -0.163  0.201  0.028 -1.080 |
## Tuotsuunta_2                               0.171  0.211  0.028  1.080 |
## Tautsu_012_0                              -0.253  0.288  0.028 -1.081 |
## Tautsu_012_1                              -0.027  0.003  0.000 -0.116 |
## Tautsu_012_2                               0.215  0.270  0.030  1.124 |
## PORSOSASTO_kertayt_0ei_0                  -0.047  0.019  0.003 -0.359 |
## PORSOSASTO_kertayt_0ei_1                   0.065  0.026  0.003  0.359 |
## 
## Categorical variables (eta2)
##                                             Dim.1 Dim.2 Dim.3  
## Haastrooli_1OmEiosall_2OmOsall_3Esimies   | 0.030 0.064 0.104 |
## Tuotsuunta                                | 0.029 0.015 0.028 |
## Tautsu_012                                | 0.058 0.016 0.038 |
## PORSOSASTO_kertayt_0ei                    | 0.210 0.013 0.003 |
## PORS_pesu_0ei                             | 0.004 0.001 0.085 |
## PORS_desinf_0ei_1LIU_2KUIVA               | 0.027 0.019 0.205 |
## PORS_tyhjana_mi1vr_0ei                    | 0.000 0.245 0.003 |
## Tuhoelmerkkeja_0kylla_1ei                 | 0.036 0.072 0.020 |
## kissoja0on1ei                             | 0.111 0.059 0.068 |
## Kotielain_sikalaan_0kylla_1ei             | 0.044 0.015 0.009 |
## 
## Supplementary continuous variables (the 10 first)
##                                              Dim.1    Dim.2    Dim.3  
## Karjut_astsiem                            |  0.182 |  0.027 | -0.002 |
## emakot                                    |  0.611 |  0.015 | -0.039 |
## ensikot                                   |  0.427 | -0.011 | -0.039 |
## lihasiat                                  |  0.139 | -0.004 |  0.040 |
## karjut                                    |  0.463 | -0.012 |  0.104 |
## kokemusave                                | -0.270 | -0.138 | -0.144 |
## kokemusmax                                | -0.116 | -0.148 | -0.124 |
## emakoitaper                               |  0.509 |  0.033 | -0.059 |
## nivelpros                                 |  0.228 |  0.074 |  0.065 |
## paisepros                                 |  0.536 |  0.078 |  0.151 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

fviz_mca_var(res_mca, choice = "mca.cor", 
            repel = TRUE, # Avoid text overlapping (slow)
            ggtheme = theme_minimal())

To visualize the percentage of inertia explained by each MCA dimension:

fviz_mca_var(res_mca, col.var = "contrib",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
             repel = TRUE, # avoid text overlapping (slow)
             ggtheme = theme_minimal()
             )

Simple bar plots can also be used to visualize contribution of variable categories. The top 12 variable categories contributing to the first and second dimension:

# Contributions of rows to dimension 1
fviz_contrib(res_mca, choice = "var", axes = 1, top = 12)

# Contributions of rows to dimension 2
fviz_contrib(res_mca, choice = "var", axes = 2, top = 12)


Medication and diseases

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="med.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 36
## $ M_parasperyear                              <fctr> 2,4, 2,3, 2,4, 2,...
## $ M_parasot_1before_2inFAR_3noinfo_4allatonce <int> 1, 2, 2, 2, 1, 3, ...
## $ M_induction_0never_1sometimes               <int> 1, 1, 1, 0, 1, 1, ...
## $ M_milkfever                                 <int> 1, 0, 0, 1, 1, 1, ...
## $ M_metritis                                  <int> 1, 0, 0, 1, 0, 1, ...
## $ M_secr                                      <int> 1, 0, 0, 0, 0, 0, ...
## $ M_mastitis                                  <int> 0, 0, 0, 1, 0, 0, ...
## $ M_lame                                      <int> 1, 0, 1, 1, 1, 0, ...
## $ M_anorex                                    <int> 1, 0, 0, 1, 1, 1, ...
## $ M_fever                                     <int> 1, 0, 0, 0, 0, 0, ...
## $ M_injury                                    <int> 1, 0, 0, 0, 0, 0, ...
## $ M_pregNSAIDS100_0_099_1                     <int> 2, 0, 1, 1, 1, 0, ...
## $ M_pregAB100_0_099_1                         <int> 2, 0, 1, 0, 1, 0, ...
## $ M_farNSAIDS100_05_630_31100                 <int> 2, 0, 0, 0, 2, 0, ...
## $ M_farAB100_05_510_10                        <int> 0, 2, 0, 0, 1, 0, ...
## $ M_routine_0no_1yes                          <int> 1, 1, 1, 1, 1, 1, ...
## $ M_routine_medic_NO                          <fctr> OX_FARNSAIDS, no,...
## $ M_rAB_NO                                    <int> 0, 1, 0, 1, 0, 0, ...
## $ M_rOX                                       <int> 1, 0, 0, 1, 1, 1, ...
## $ M_rIND_NO                                   <int> 0, 0, 0, 0, 1, 0, ...
## $ M_rFARNSAIDS_NO                             <int> 1, 0, 1, 0, 0, 1, ...
## $ M_OX_10far_NUM_NO                           <fctr> 10, 3, 2, 2, 10, ...
## $ M_OX_obstex_preox                           <fctr> 1, 0, 0, 1, 0, 0,...
## $ M_OX_dosage_NO                              <fctr> 0,8, 0,5, 0,5, 0,...
## $ M_OX_between_NUM_NO                         <fctr> 0,5, 0,5, 2, 0,5,...
## $ M_OX_max_NUM_NO                             <fctr> 2,5, 4, 0,5, 3,5,...
## $ M_FAR_assist_NUM_NO                         <fctr> 50, 20, 5, 25, no...
## $ M_farNSAIDS_0no_1rout_2ifneed_NO            <fctr> 1, 0, 1, 2, 2, 1,...
## $ M_lameness_NO                               <fctr> NSAIDS_PEN, 0, NS...
## $ M_AB_effectave_NUM_NO                       <fctr> 2,86, 4, 2,5, 3, ...
## $ OUT_SOW_mort_proNUM                         <int> 5, 5, 8, 27, 10, 0...
## $ OUT_SOW_mort_dic                            <int> 0, 0, 0, 1, 1, 0, ...
## $ OUT_SOW_totremproNUM                        <int> 34, 38, 53, 57, 65...
## $ OUT_SOW_totrem_dic                          <int> 0, 0, 1, 1, 1, 1, ...
## $ OUT_SOW_cullproNUM                          <int> 29, 33, 45, 30, 55...
## $ OUT_SOW_cull_dic                            <int> 0, 0, 1, 0, 1, 1, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca        
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

colnames(X)[ apply(X, 2, anyNA) ]
## character(0)
X$M_induction_0never_1sometimes<-as.factor(X$M_induction_0never_1sometimes)
X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="purple") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
M_OX_10far_NUM_NO (mean (sd)) 5.26 (3.11) 5.05 (3.03) 0.824
M_OX_between_NUM_NO (mean (sd)) 3.96 (2.18) 4.95 (2.80) 0.199
M_OX_max_NUM_NO (mean (sd)) 6.00 (2.37) 6.55 (2.16) 0.434
M_FAR_assist_NUM_NO (mean (sd)) 3.96 (3.04) 6.20 (3.32) 0.026
M_AB_effectave_NUM_NO (mean (sd)) 8.22 (3.16) 7.95 (3.66) 0.798
M_OX_dosage_NO (mean (sd)) 5.26 (1.54) 6.20 (2.02) 0.092
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
M_parasperyear (%) 0.322
0 0 ( 0.0) 2 ( 10.0)
1 1 ( 4.3) 0 ( 0.0)
2 2 ( 8.7) 1 ( 5.0)
2,1 1 ( 4.3) 0 ( 0.0)
2,2 0 ( 0.0) 1 ( 5.0)
2,3 8 (34.8) 7 ( 35.0)
2,4 11 (47.8) 7 ( 35.0)
4 0 ( 0.0) 2 ( 10.0)
M_parasot_1before_2inFAR_3noinfo_4allatonce (%) 0.743
1 10 (43.5) 9 ( 45.0)
2 9 (39.1) 7 ( 35.0)
3 4 (17.4) 3 ( 15.0)
4 0 ( 0.0) 1 ( 5.0)
M_induction_0never_1sometimes (%) 0.433
0 10 (43.5) 6 ( 31.6)
1 12 (52.2) 13 ( 68.4)
2 1 ( 4.3) 0 ( 0.0)
M_milkfever = 1 (%) 12 (52.2) 10 ( 50.0) 1.000
M_metritis = 1 (%) 10 (43.5) 9 ( 45.0) 1.000
M_secr = 1 (%) 2 ( 8.7) 3 ( 15.0) 0.868
M_mastitis = 1 (%) 4 (17.4) 6 ( 30.0) 0.539
M_lame = 1 (%) 15 (65.2) 16 ( 80.0) 0.461
M_anorex = 1 (%) 10 (43.5) 12 ( 60.0) 0.438
M_fever = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
M_injury = 1 (%) 10 (43.5) 6 ( 30.0) 0.551
M_pregNSAIDS100_0_099_1 (%) 0.130
0 10 (45.5) 5 ( 26.3)
1 4 (18.2) 9 ( 47.4)
2 8 (36.4) 5 ( 26.3)
M_pregAB100_0_099_1 (%) 0.514
0 9 (40.9) 5 ( 26.3)
1 5 (22.7) 7 ( 36.8)
2 8 (36.4) 7 ( 36.8)
M_farNSAIDS100_05_630_31100 (%) 0.409
0 7 (31.8) 7 ( 36.8)
1 10 (45.5) 5 ( 26.3)
2 5 (22.7) 7 ( 36.8)
M_farAB100_05_510_10 (%) 0.273
0 12 (54.5) 10 ( 52.6)
1 8 (36.4) 4 ( 21.1)
2 2 ( 9.1) 5 ( 26.3)
M_routine_0no_1yes = 1 (%) 15 (65.2) 13 ( 65.0) 1.000
M_routine_medic_NO (%) 0.265
_COC 1 ( 4.3) 0 ( 0.0)
_FARNSAIDS 3 (13.0) 2 ( 10.0)
_FARNSAIDS_COC 1 ( 4.3) 0 ( 0.0)
_FARNSAIDS_PPAB 0 ( 0.0) 1 ( 5.0)
_PPAB 1 ( 4.3) 1 ( 5.0)
no 9 (39.1) 7 ( 35.0)
OX 6 (26.1) 3 ( 15.0)
OX_FARNSAIDS 2 ( 8.7) 0 ( 0.0)
OX_FARNSAIDS_COC_PPAB 0 ( 0.0) 1 ( 5.0)
OX_IND 0 ( 0.0) 4 ( 20.0)
OX_PPAB 0 ( 0.0) 1 ( 5.0)
M_rAB_NO = 1 (%) 2 ( 8.7) 4 ( 20.0) 0.531
M_rOX = 1 (%) 8 (34.8) 9 ( 45.0) 0.711
M_rIND_NO = 1 (%) 0 ( 0.0) 4 ( 20.0) 0.084
M_rFARNSAIDS_NO = 1 (%) 6 (26.1) 4 ( 20.0) 0.913
M_OX_obstex_preox (%) 0.493
0 15 (65.2) 11 ( 55.0)
1 8 (34.8) 8 ( 40.0)
noinfo 0 ( 0.0) 1 ( 5.0)
M_farNSAIDS_0no_1rout_2ifneed_NO (%) 0.667
0 1 ( 4.3) 0 ( 0.0)
1 6 (26.1) 4 ( 20.0)
2 15 (65.2) 14 ( 70.0)
noinfo 1 ( 4.3) 2 ( 10.0)
M_lameness_NO (%) 0.249
_PEN 0 ( 0.0) 1 ( 5.0)
0 9 (39.1) 5 ( 25.0)
3 0 ( 0.0) 2 ( 10.0)
NSAIDS 1 ( 4.3) 1 ( 5.0)
NSAIDS_AMOX 1 ( 4.3) 0 ( 0.0)
NSAIDS_PEN 10 (43.5) 4 ( 20.0)
NSAIDS_PEN_AMOX 0 ( 0.0) 2 ( 10.0)
NSAIDS_PEN_SEL 0 ( 0.0) 1 ( 5.0)
NSAIDS_PEN_TRIM 0 ( 0.0) 1 ( 5.0)
NSAIDS_TETR 0 ( 0.0) 1 ( 5.0)
NSAIDS3 2 ( 8.7) 2 ( 10.0)
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
M_OX_10far_NUM_NO (mean (sd)) 5.27 (3.10) 5.05 (3.04) 0.811
M_OX_between_NUM_NO (mean (sd)) 4.18 (2.36) 4.67 (2.69) 0.533
M_OX_max_NUM_NO (mean (sd)) 6.00 (2.12) 6.52 (2.44) 0.456
M_FAR_assist_NUM_NO (mean (sd)) 5.00 (3.21) 5.00 (3.54) 1.000
M_AB_effectave_NUM_NO (mean (sd)) 7.59 (3.49) 8.62 (3.23) 0.322
M_OX_dosage_NO (mean (sd)) 5.45 (1.77) 5.95 (1.88) 0.376
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
M_parasperyear (%) 0.267
0 2 ( 9.1) 0 ( 0.0)
1 1 ( 4.5) 0 ( 0.0)
2 1 ( 4.5) 2 ( 9.5)
2,1 1 ( 4.5) 0 ( 0.0)
2,2 1 ( 4.5) 0 ( 0.0)
2,3 9 (40.9) 6 ( 28.6)
2,4 7 (31.8) 11 ( 52.4)
4 0 ( 0.0) 2 ( 9.5)
M_parasot_1before_2inFAR_3noinfo_4allatonce (%) 0.760
1 9 (40.9) 10 ( 47.6)
2 8 (36.4) 8 ( 38.1)
3 4 (18.2) 3 ( 14.3)
4 1 ( 4.5) 0 ( 0.0)
M_induction_0never_1sometimes (%) 0.525
0 9 (42.9) 7 ( 33.3)
1 12 (57.1) 13 ( 61.9)
2 0 ( 0.0) 1 ( 4.8)
M_milkfever = 1 (%) 12 (54.5) 10 ( 47.6) 0.882
M_metritis = 1 (%) 12 (54.5) 7 ( 33.3) 0.274
M_secr = 1 (%) 2 ( 9.1) 3 ( 14.3) 0.956
M_mastitis = 1 (%) 4 (18.2) 6 ( 28.6) 0.656
M_lame = 1 (%) 16 (72.7) 15 ( 71.4) 1.000
M_anorex = 1 (%) 13 (59.1) 9 ( 42.9) 0.448
M_fever = 1 (%) 4 (18.2) 2 ( 9.5) 0.705
M_injury = 1 (%) 8 (36.4) 8 ( 38.1) 1.000
M_pregNSAIDS100_0_099_1 (%) 0.510
0 9 (42.9) 6 ( 30.0)
1 5 (23.8) 8 ( 40.0)
2 7 (33.3) 6 ( 30.0)
M_pregAB100_0_099_1 (%) 0.849
0 8 (38.1) 6 ( 30.0)
1 6 (28.6) 6 ( 30.0)
2 7 (33.3) 8 ( 40.0)
M_farNSAIDS100_05_630_31100 (%) 0.284
0 9 (42.9) 5 ( 25.0)
1 8 (38.1) 7 ( 35.0)
2 4 (19.0) 8 ( 40.0)
M_farAB100_05_510_10 (%) 0.049
0 15 (71.4) 7 ( 35.0)
1 3 (14.3) 9 ( 45.0)
2 3 (14.3) 4 ( 20.0)
M_routine_0no_1yes = 1 (%) 12 (54.5) 16 ( 76.2) 0.243
M_routine_medic_NO (%) 0.222
_COC 0 ( 0.0) 1 ( 4.8)
_FARNSAIDS 4 (18.2) 1 ( 4.8)
_FARNSAIDS_COC 0 ( 0.0) 1 ( 4.8)
_FARNSAIDS_PPAB 0 ( 0.0) 1 ( 4.8)
_PPAB 0 ( 0.0) 2 ( 9.5)
no 11 (50.0) 5 ( 23.8)
OX 3 (13.6) 6 ( 28.6)
OX_FARNSAIDS 1 ( 4.5) 1 ( 4.8)
OX_FARNSAIDS_COC_PPAB 1 ( 4.5) 0 ( 0.0)
OX_IND 1 ( 4.5) 3 ( 14.3)
OX_PPAB 1 ( 4.5) 0 ( 0.0)
M_rAB_NO = 1 (%) 3 (13.6) 3 ( 14.3) 1.000
M_rOX = 1 (%) 7 (31.8) 10 ( 47.6) 0.455
M_rIND_NO = 1 (%) 1 ( 4.5) 3 ( 14.3) 0.566
M_rFARNSAIDS_NO = 1 (%) 6 (27.3) 4 ( 19.0) 0.782
M_OX_obstex_preox (%) 0.541
0 13 (59.1) 13 ( 61.9)
1 9 (40.9) 7 ( 33.3)
noinfo 0 ( 0.0) 1 ( 4.8)
M_farNSAIDS_0no_1rout_2ifneed_NO (%) 0.627
0 1 ( 4.5) 0 ( 0.0)
1 6 (27.3) 4 ( 19.0)
2 14 (63.6) 15 ( 71.4)
noinfo 1 ( 4.5) 2 ( 9.5)
M_lameness_NO (%) 0.700
_PEN 1 ( 4.5) 0 ( 0.0)
0 8 (36.4) 6 ( 28.6)
3 1 ( 4.5) 1 ( 4.8)
NSAIDS 1 ( 4.5) 1 ( 4.8)
NSAIDS_AMOX 0 ( 0.0) 1 ( 4.8)
NSAIDS_PEN 7 (31.8) 7 ( 33.3)
NSAIDS_PEN_AMOX 0 ( 0.0) 2 ( 9.5)
NSAIDS_PEN_SEL 1 ( 4.5) 0 ( 0.0)
NSAIDS_PEN_TRIM 1 ( 4.5) 0 ( 0.0)
NSAIDS_TETR 0 ( 0.0) 1 ( 4.8)
NSAIDS3 2 ( 9.1) 2 ( 9.5)
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(20,21),quali.sup=c(17:19), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(20, 21), quali.sup = c(17:19),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.335   0.214   0.164   0.126   0.113   0.093
## % of var.             19.858  12.678   9.700   7.458   6.705   5.538
## Cumulative % of var.  19.858  32.536  42.236  49.693  56.398  61.937
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.086   0.079   0.071   0.065   0.056   0.047
## % of var.              5.076   4.708   4.193   3.841   3.298   2.799
## Cumulative % of var.  67.012  71.721  75.913  79.754  83.052  85.851
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.040   0.035   0.032   0.026   0.022   0.020
## % of var.              2.373   2.085   1.882   1.534   1.295   1.192
## Cumulative % of var.  88.224  90.309  92.192  93.725  95.020  96.212
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.017   0.014   0.012   0.009   0.007   0.004
## % of var.              1.034   0.830   0.694   0.533   0.440   0.257
## Cumulative % of var.  97.246  98.077  98.771  99.303  99.743 100.000
##                       Dim.25  Dim.26  Dim.27
## Variance               0.000   0.000   0.000
## % of var.              0.000   0.000   0.000
## Cumulative % of var. 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                                     Dim.1    ctr   cos2    Dim.2    ctr
## 1                                | -0.581  2.340  0.171 |  0.728  5.765
## 2                                |  0.136  0.128  0.015 | -0.275  0.820
## 3                                | -0.010  0.001  0.000 | -0.295  0.947
## 4                                | -0.151  0.159  0.018 | -0.413  1.855
## 5                                | -0.287  0.570  0.074 |  0.191  0.398
## 6                                | -0.051  0.018  0.002 | -0.376  1.538
## 7                                |  0.123  0.106  0.009 |  0.031  0.010
## 8                                | -0.067  0.032  0.004 | -0.408  1.810
## 9                                |  0.212  0.313  0.043 | -0.667  4.831
## 10                               | -0.317  0.696  0.088 |  0.161  0.282
##                                    cos2    Dim.3    ctr   cos2  
## 1                                 0.269 |  0.395  2.217  0.079 |
## 2                                 0.059 |  0.503  3.591  0.198 |
## 3                                 0.093 | -0.194  0.534  0.040 |
## 4                                 0.133 | -0.439  2.739  0.150 |
## 5                                 0.033 | -0.577  4.731  0.300 |
## 6                                 0.130 |  0.142  0.288  0.019 |
## 7                                 0.001 |  0.726  7.491  0.309 |
## 8                                 0.155 | -0.473  3.173  0.207 |
## 9                                 0.424 |  0.283  1.135  0.076 |
## 10                                0.023 | -0.422  2.529  0.156 |
## 
## Categories (the 10 first)
##                                     Dim.1    ctr   cos2 v.test    Dim.2
## M_induction_0never_1sometimes.NA |  0.320  0.044  0.002  0.320 | -1.507
## M_induction_0never_1sometimes_0  |  0.564  2.210  0.189  2.815 | -0.362
## M_induction_0never_1sometimes_1  | -0.356  1.374  0.176 -2.718 |  0.255
## M_induction_0never_1sometimes_2  | -0.450  0.088  0.005 -0.450 |  0.922
## M_milkfever_0                    |  0.013  0.002  0.000  0.084 |  0.009
## M_milkfever_1                    | -0.013  0.002  0.000 -0.084 | -0.009
## M_metritis_0                     |  0.259  0.699  0.085  1.888 | -0.029
## M_metritis_1                     | -0.327  0.883  0.085 -1.888 |  0.036
## M_secr_0                         |  0.105  0.180  0.083  1.869 | -0.216
## M_secr_1                         | -0.795  1.371  0.083 -1.869 |  1.642
##                                     ctr   cos2 v.test    Dim.3    ctr
## M_induction_0never_1sometimes.NA  1.542  0.054 -1.507 |  3.162  8.880
## M_induction_0never_1sometimes_0   1.422  0.078 -1.804 | -0.087  0.109
## M_induction_0never_1sometimes_1   1.103  0.090  1.946 | -0.048  0.052
## M_induction_0never_1sometimes_2   0.578  0.020  0.922 | -0.555  0.274
## M_milkfever_0                     0.001  0.000  0.057 |  0.419  3.268
## M_milkfever_1                     0.001  0.000 -0.057 | -0.400  3.119
## M_metritis_0                      0.013  0.001 -0.208 |  0.057  0.068
## M_metritis_1                      0.017  0.001  0.208 | -0.071  0.086
## M_secr_0                          1.205  0.355 -3.860 | -0.140  0.661
## M_secr_1                          9.158  0.355  3.860 |  1.064  5.025
##                                    cos2 v.test  
## M_induction_0never_1sometimes.NA  0.238  3.162 |
## M_induction_0never_1sometimes_0   0.005 -0.436 |
## M_induction_0never_1sometimes_1   0.003 -0.369 |
## M_induction_0never_1sometimes_2   0.007 -0.555 |
## M_milkfever_0                     0.167  2.651 |
## M_milkfever_1                     0.167 -2.651 |
## M_metritis_0                      0.004  0.412 |
## M_metritis_1                      0.004 -0.412 |
## M_secr_0                          0.149 -2.501 |
## M_secr_1                          0.149  2.501 |
## 
## Categorical variables (eta2)
##                                    Dim.1 Dim.2 Dim.3  
## M_induction_0never_1sometimes    | 0.199 0.159 0.244 |
## M_milkfever                      | 0.000 0.000 0.167 |
## M_metritis                       | 0.085 0.001 0.004 |
## M_secr                           | 0.083 0.355 0.149 |
## M_mastitis                       | 0.010 0.006 0.118 |
## M_lame                           | 0.229 0.001 0.152 |
## M_anorex                         | 0.115 0.001 0.033 |
## M_fever                          | 0.011 0.015 0.417 |
## M_injury                         | 0.108 0.136 0.116 |
## M_pregNSAIDS100_0_099_1          | 0.906 0.538 0.416 |
## 
## Supplementary categories
##                                     Dim.1   cos2 v.test    Dim.2   cos2
## OUT_SOW_mort_dic_0               |  0.026  0.001  0.180 | -0.077  0.007
## OUT_SOW_mort_dic_1               | -0.030  0.001 -0.180 |  0.088  0.007
## OUT_SOW_totrem_dic_0             |  0.013  0.000  0.084 | -0.036  0.001
## OUT_SOW_totrem_dic_1             | -0.013  0.000 -0.084 |  0.034  0.001
## OUT_SOW_cull_dic_0               |  0.029  0.001  0.192 | -0.216  0.049
## OUT_SOW_cull_dic_1               | -0.030  0.001 -0.192 |  0.226  0.049
##                                  v.test    Dim.3   cos2 v.test  
## OUT_SOW_mort_dic_0               -0.533 |  0.041  0.002  0.283 |
## OUT_SOW_mort_dic_1                0.533 | -0.047  0.002 -0.283 |
## OUT_SOW_totrem_dic_0             -0.227 |  0.197  0.037  1.250 |
## OUT_SOW_totrem_dic_1              0.227 | -0.188  0.037 -1.250 |
## OUT_SOW_cull_dic_0               -1.429 |  0.146  0.022  0.967 |
## OUT_SOW_cull_dic_1                1.429 | -0.153  0.022 -0.967 |
## 
## Supplementary categorical variables (eta2)
##                                    Dim.1 Dim.2 Dim.3  
## OUT_SOW_mort_dic                 | 0.001 0.007 0.002 |
## OUT_SOW_totrem_dic               | 0.000 0.001 0.037 |
## OUT_SOW_cull_dic                 | 0.001 0.049 0.022 |
## 
## Supplementary continuous variables
##                                     Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM              | -0.105 |  0.140 | -0.129 |
## OUT_SOW_cullproNUM               | -0.002 |  0.237 | -0.300 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("13", "21", "24", "43", "40", "9", "16", "1", "34", "41")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by1* and *1*. :
## 
## - high frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_2*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_2*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_2*, *M_routine_0no_1yes=M_routine_0no_1yes_1*, *M_injury=M_injury_1*, *M_rOX=M_rOX_1*, *M_farAB100_05_510_10=M_farAB100_05_510_10_1*, *M_secr=M_secr_1* and *M_induction_0never_1sometimes=M_induction_0never_1sometimes_1* (factors are sorted from the most common).
## - low frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_0*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_0*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_0*, *M_routine_0no_1yes=M_routine_0no_1yes_0*, *M_injury=M_injury_0*, *M_rOX=M_rOX_0*, *M_farAB100_05_510_10=M_farAB100_05_510_10_0*, *M_induction_0never_1sometimes=M_induction_0never_1sometimes_0* and *M_secr=M_secr_0* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by9* and *9*. :
## 
## - high frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_0*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_0*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_0*, *M_farAB100_05_510_10=M_farAB100_05_510_10_0*, *M_injury=M_injury_0*, *M_routine_0no_1yes=M_routine_0no_1yes_0*, *M_rOX=M_rOX_0* and *M_secr=M_secr_0* (factors are sorted from the most common).
## - low frequency for the factors *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100_2*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1_2*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1_2*, *M_injury=M_injury_1*, *M_routine_0no_1yes=M_routine_0no_1yes_1*, *M_farAB100_05_510_10=M_farAB100_05_510_10_1*, *M_rOX=M_rOX_1* and *M_secr=M_secr_1* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by13* and *13*. :
## 
## - high frequency for the factors *M_farAB100_05_510_10=M_farAB100_05_510_10.NA*, *M_farNSAIDS100_05_630_31100=M_farNSAIDS100_05_630_31100.NA*, *M_pregAB100_0_099_1=M_pregAB100_0_099_1.NA*, *M_pregNSAIDS100_0_099_1=M_pregNSAIDS100_0_099_1.NA* and *M_OX_obstex_preox=M_OX_obstex_preox_noinfo* (factors are sorted from the most common).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Biosecurity

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="bio.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 27
## $ B_Biosec                   <int> 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,...
## $ B_Biosecused               <int> 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,...
## $ B_Biosec_012               <int> 2, 0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1,...
## $ B_Pests                    <int> 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,...
## $ B_Entrancehuman            <fctr> 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1...
## $ B_Entranceanimal           <fctr> 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1...
## $ B_Handswash                <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,...
## $ B_Bootswash                <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,...
## $ B_Loadingbay               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ B_Entrancedriver           <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,...
## $ B_carcasstruckenter        <int> 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,...
## $ B_pestentercarcass         <fctr> no, yes, yes, no, no, no, no, yes,...
## $ B_pestcontrol              <fctr> catpois, catpois, catpoistrap, poi...
## $ B_pestsigns                <fctr> yes, yes, yes, no, yes, yes, yes, ...
## $ B_birds                    <fctr> yes, yes, yes, no, no, no, no, no,...
## $ B_pestcontrolplan          <fctr> yes, no, no, no, yes, no, no, no, ...
## $ B_cats                     <fctr> yes, yes, yes, no, no, yes, no, no...
## $ B_pets_in                  <fctr> yes, yes, no, no, no, yes, no, no,...
## $ B_biosecsumNUM_NO          <int> 18, 14, 12, 16, 15, 12, 12, 8, 10, ...
## $ B_EXT_BIOSEC_SCORE_NUM_NO  <dbl> 10.0, 8.0, 8.0, 8.0, 7.0, 8.0, 6.0,...
## $ B_INT_BIOSEC_SCOREB_NUM_NO <dbl> 8.0, 6.0, 1.0, 8.0, 6.0, 3.0, 5.0, ...
## $ OUT_SOW_mort_proNUM        <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9...
## $ OUT_SOW_mort_dic           <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,...
## $ OUT_SOW_totremproNUM       <int> 34, 38, 53, 57, 65, 64, 47, 44, 24,...
## $ OUT_SOW_totrem_dic         <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,...
## $ OUT_SOW_cullproNUM         <int> 29, 33, 45, 30, 55, 64, 30, 31, 24,...
## $ OUT_SOW_cull_dic           <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,...
med<-med%>%mutate_all(as.factor)

med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("B_pestcont")))
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="pink") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
B_biosecsumNUM_NO (mean (sd)) 7.78 (3.52) 8.35 (3.13) 0.582
B_EXT_BIOSEC_SCORE_NUM_NO (mean (sd)) 6.27 (2.66) 6.45 (2.31) 0.819
B_INT_BIOSEC_SCOREB_NUM_NO (mean (sd)) 7.48 (4.40) 9.30 (3.23) 0.134
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
B_Biosec = 1 (%) 15 (65.2) 14 ( 70.0) 0.994
B_Biosecused = 1 (%) 9 (39.1) 12 ( 60.0) 0.289
B_Biosec_012 (%) 0.731
0 8 (36.4) 5 ( 26.3)
1 6 (27.3) 5 ( 26.3)
2 8 (36.4) 9 ( 47.4)
B_Pests = 1 (%) 16 (69.6) 19 ( 95.0) 0.081
B_Entrancehuman (%) 0.296
0 7 (30.4) 9 ( 45.0)
1 16 (69.6) 10 ( 50.0)
n 0 ( 0.0) 1 ( 5.0)
B_Entranceanimal (%) 0.251
0 9 (39.1) 4 ( 20.0)
1 14 (60.9) 15 ( 75.0)
y 0 ( 0.0) 1 ( 5.0)
B_Handswash = 1 (%) 14 (60.9) 16 ( 80.0) 0.303
B_Bootswash = 1 (%) 14 (60.9) 17 ( 85.0) 0.156
B_Loadingbay = 1 (%) 19 (82.6) 17 ( 85.0) 1.000
B_Entrancedriver = 1 (%) 17 (73.9) 13 ( 65.0) 0.763
B_carcasstruckenter (%) 0.435
0 7 (30.4) 9 ( 45.0)
1 15 (65.2) 11 ( 55.0)
2 1 ( 4.3) 0 ( 0.0)
B_pestentercarcass (%) 0.395
1 ( 4.3) 0 ( 0.0)
no 11 (47.8) 13 ( 65.0)
yes 11 (47.8) 7 ( 35.0)
B_pestcontrol (%) 0.158
catdogpois 0 ( 0.0) 1 ( 5.0)
catdogpoistrap 1 ( 4.3) 0 ( 0.0)
catdogpoistrapfirm 1 ( 4.3) 0 ( 0.0)
catpois 6 (26.1) 4 ( 20.0)
catpoisother 0 ( 0.0) 1 ( 5.0)
catpoistrap 5 (21.7) 0 ( 0.0)
catpoistrapother 1 ( 4.3) 0 ( 0.0)
nothing 0 ( 0.0) 1 ( 5.0)
pois 7 (30.4) 6 ( 30.0)
poistrap 2 ( 8.7) 6 ( 30.0)
trap 0 ( 0.0) 1 ( 5.0)
B_pestsigns (%) 0.428
no 5 (21.7) 6 ( 30.0)
no0 0 ( 0.0) 1 ( 5.0)
yes 18 (78.3) 13 ( 65.0)
B_birds (%) 0.555
no 17 (73.9) 14 ( 70.0)
no 1 ( 4.3) 0 ( 0.0)
yes 5 (21.7) 6 ( 30.0)
B_pestcontrolplan (%) 0.168
no 19 (82.6) 15 ( 75.0)
no 2 ( 8.7) 0 ( 0.0)
yes 2 ( 8.7) 5 ( 25.0)
B_cats = yes (%) 19 (82.6) 8 ( 40.0) 0.010
B_pets_in = yes (%) 6 (26.1) 5 ( 25.0) 1.000
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
B_biosecsumNUM_NO (mean (sd)) 8.50 (3.28) 7.57 (3.37) 0.365
B_EXT_BIOSEC_SCORE_NUM_NO (mean (sd)) 6.71 (2.63) 6.00 (2.30) 0.355
B_INT_BIOSEC_SCOREB_NUM_NO (mean (sd)) 9.18 (3.65) 7.43 (4.17) 0.149
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
B_Biosec = 1 (%) 14 (63.6) 15 ( 71.4) 0.826
B_Biosecused = 1 (%) 11 (50.0) 10 ( 47.6) 1.000
B_Biosec_012 (%) 0.904
0 7 (33.3) 6 ( 30.0)
1 5 (23.8) 6 ( 30.0)
2 9 (42.9) 8 ( 40.0)
B_Pests = 1 (%) 17 (77.3) 18 ( 85.7) 0.750
B_Entrancehuman (%) 0.398
0 7 (31.8) 9 ( 42.9)
1 15 (68.2) 11 ( 52.4)
n 0 ( 0.0) 1 ( 4.8)
B_Entranceanimal (%) 0.580
0 7 (31.8) 6 ( 28.6)
1 15 (68.2) 14 ( 66.7)
y 0 ( 0.0) 1 ( 4.8)
B_Handswash = 1 (%) 15 (68.2) 15 ( 71.4) 1.000
B_Bootswash = 1 (%) 16 (72.7) 15 ( 71.4) 1.000
B_Loadingbay = 1 (%) 16 (72.7) 20 ( 95.2) 0.113
B_Entrancedriver = 1 (%) 17 (77.3) 13 ( 61.9) 0.444
B_carcasstruckenter (%) 0.613
0 8 (36.4) 8 ( 38.1)
1 13 (59.1) 13 ( 61.9)
2 1 ( 4.5) 0 ( 0.0)
B_pestentercarcass (%) 0.186
0 ( 0.0) 1 ( 4.8)
no 15 (68.2) 9 ( 42.9)
yes 7 (31.8) 11 ( 52.4)
B_pestcontrol (%) 0.602
catdogpois 0 ( 0.0) 1 ( 4.8)
catdogpoistrap 0 ( 0.0) 1 ( 4.8)
catdogpoistrapfirm 0 ( 0.0) 1 ( 4.8)
catpois 6 (27.3) 4 ( 19.0)
catpoisother 1 ( 4.5) 0 ( 0.0)
catpoistrap 2 ( 9.1) 3 ( 14.3)
catpoistrapother 0 ( 0.0) 1 ( 4.8)
nothing 1 ( 4.5) 0 ( 0.0)
pois 8 (36.4) 5 ( 23.8)
poistrap 4 (18.2) 4 ( 19.0)
trap 0 ( 0.0) 1 ( 4.8)
B_pestsigns (%) 0.577
no 6 (27.3) 5 ( 23.8)
no0 0 ( 0.0) 1 ( 4.8)
yes 16 (72.7) 15 ( 71.4)
B_birds (%) 0.577
no 16 (72.7) 15 ( 71.4)
no 0 ( 0.0) 1 ( 4.8)
yes 6 (27.3) 5 ( 23.8)
B_pestcontrolplan (%) 0.420
no 19 (86.4) 15 ( 71.4)
no 1 ( 4.5) 1 ( 4.8)
yes 2 ( 9.1) 5 ( 23.8)
B_cats = yes (%) 12 (54.5) 15 ( 71.4) 0.407
B_pets_in = yes (%) 4 (18.2) 7 ( 33.3) 0.430
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(20,21),quali.sup=c(17:19), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(20, 21), quali.sup = c(17:19),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.218   0.161   0.142   0.128   0.106   0.098
## % of var.             14.555  10.729   9.496   8.513   7.056   6.533
## Cumulative % of var.  14.555  25.284  34.780  43.292  50.348  56.881
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.095   0.082   0.073   0.065   0.056   0.051
## % of var.              6.351   5.434   4.892   4.312   3.737   3.413
## Cumulative % of var.  63.231  68.665  73.558  77.870  81.606  85.019
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.046   0.039   0.037   0.028   0.024   0.018
## % of var.              3.084   2.593   2.493   1.858   1.576   1.214
## Cumulative % of var.  88.103  90.696  93.189  95.046  96.623  97.837
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.014   0.012   0.004   0.002   0.000   0.000
## % of var.              0.954   0.830   0.254   0.125   0.000   0.000
## Cumulative % of var.  98.791  99.621  99.875 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                         Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                    |  0.145  0.224  0.021 | -0.221  0.706  0.049 |
## 2                    | -0.484  2.498  0.178 |  0.404  2.361  0.124 |
## 3                    |  0.246  0.643  0.051 |  0.127  0.232  0.013 |
## 4                    |  0.232  0.574  0.054 | -0.344  1.711  0.118 |
## 5                    |  0.256  0.696  0.066 | -0.347  1.738  0.121 |
## 6                    | -0.125  0.167  0.011 | -0.071  0.073  0.004 |
## 7                    | -0.333  1.182  0.116 |  0.184  0.490  0.035 |
## 8                    | -0.146  0.226  0.015 |  0.310  1.386  0.066 |
## 9                    | -0.275  0.805  0.066 |  0.220  0.702  0.042 |
## 10                   | -0.233  0.578  0.056 | -0.227  0.747  0.053 |
##                       Dim.3    ctr   cos2  
## 1                    -0.126  0.261  0.016 |
## 2                     0.152  0.379  0.018 |
## 3                    -0.706  8.144  0.418 |
## 4                     0.136  0.301  0.018 |
## 5                    -0.192  0.603  0.037 |
## 6                    -0.387  2.449  0.110 |
## 7                     0.195  0.620  0.040 |
## 8                    -0.322  1.698  0.071 |
## 9                     0.227  0.842  0.045 |
## 10                    0.298  1.450  0.092 |
## 
## Categories (the 10 first)
##                         Dim.1    ctr   cos2 v.test    Dim.2    ctr   cos2
## B_Biosec_0           | -0.876  7.160  0.371 -3.947 |  0.747  7.056  0.269
## B_Biosec_1           |  0.423  3.457  0.371  3.947 | -0.361  3.406  0.269
## B_Biosecused_0       | -0.719  7.578  0.542 -4.771 |  0.351  2.449  0.129
## B_Biosecused_1       |  0.754  7.939  0.542  4.771 | -0.368  2.566  0.129
## B_Biosec_012.NA      |  3.220 13.802  0.506  4.608 |  2.755 13.714  0.370
## B_Biosec_012_0       | -0.982  8.348  0.418 -4.190 |  0.870  8.896  0.328
## B_Biosec_012_1       | -0.218  0.348  0.016 -0.828 | -0.402  1.604  0.055
## B_Biosec_012_2       |  0.513  2.981  0.172  2.690 | -0.730  8.179  0.348
## B_Pests_0            | -0.515  1.411  0.061 -1.595 |  0.290  0.610  0.019
## B_Pests_1            |  0.118  0.322  0.061  1.595 | -0.066  0.139  0.019
##                      v.test    Dim.3    ctr   cos2 v.test  
## B_Biosec_0            3.364 |  0.229  0.748  0.025  1.030 |
## B_Biosec_1           -3.364 | -0.110  0.361  0.025 -1.030 |
## B_Biosecused_0        2.329 |  0.206  0.948  0.044  1.363 |
## B_Biosecused_1       -2.329 | -0.215  0.994  0.044 -1.363 |
## B_Biosec_012.NA       3.944 |  0.172  0.060  0.001  0.246 |
## B_Biosec_012_0        3.714 |  0.219  0.634  0.021  0.933 |
## B_Biosec_012_1       -1.527 |  0.153  0.262  0.008  0.581 |
## B_Biosec_012_2       -3.825 | -0.286  1.422  0.054 -1.500 |
## B_Pests_0             0.900 | -0.110  0.099  0.003 -0.342 |
## B_Pests_1            -0.900 |  0.025  0.023  0.003  0.342 |
## 
## Categorical variables (eta2)
##                        Dim.1 Dim.2 Dim.3  
## B_Biosec             | 0.371 0.269 0.025 |
## B_Biosecused         | 0.542 0.129 0.044 |
## B_Biosec_012         | 0.890 0.834 0.054 |
## B_Pests              | 0.061 0.019 0.003 |
## B_Entrancehuman      | 0.412 0.302 0.324 |
## B_Entranceanimal     | 0.406 0.266 0.430 |
## B_Handswash          | 0.011 0.101 0.305 |
## B_Bootswash          | 0.000 0.077 0.409 |
## B_Loadingbay         | 0.006 0.009 0.012 |
## B_Entrancedriver     | 0.013 0.000 0.100 |
## 
## Supplementary categories
##                         Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## OUT_SOW_mort_dic_0   | -0.170  0.033 -1.182 |  0.154  0.027  1.071 |
## OUT_SOW_mort_dic_1   |  0.196  0.033  1.182 | -0.177  0.027 -1.071 |
## OUT_SOW_totrem_dic_0 | -0.095  0.009 -0.604 |  0.084  0.007  0.529 |
## OUT_SOW_totrem_dic_1 |  0.091  0.009  0.604 | -0.080  0.007 -0.529 |
## OUT_SOW_cull_dic_0   |  0.015  0.000  0.098 | -0.080  0.007 -0.532 |
## OUT_SOW_cull_dic_1   | -0.015  0.000 -0.098 |  0.084  0.007  0.532 |
##                       Dim.3   cos2 v.test  
## OUT_SOW_mort_dic_0   -0.192  0.043 -1.336 |
## OUT_SOW_mort_dic_1    0.221  0.043  1.336 |
## OUT_SOW_totrem_dic_0 -0.071  0.005 -0.448 |
## OUT_SOW_totrem_dic_1  0.068  0.005  0.448 |
## OUT_SOW_cull_dic_0   -0.100  0.010 -0.663 |
## OUT_SOW_cull_dic_1    0.105  0.010  0.663 |
## 
## Supplementary categorical variables (eta2)
##                        Dim.1 Dim.2 Dim.3  
## OUT_SOW_mort_dic     | 0.033 0.027 0.043 |
## OUT_SOW_totrem_dic   | 0.009 0.007 0.005 |
## OUT_SOW_cull_dic     | 0.000 0.007 0.010 |
## 
## Supplementary continuous variables
##                         Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM  |  0.125 | -0.186 |  0.102 |
## OUT_SOW_cullproNUM   | -0.026 |  0.056 |  0.190 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("36", "21", "43", "16", "15", "30", "11", "19", "34", "2")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by2* and *2*. :
## 
## - high frequency for the factors *B_Biosecused=B_Biosecused_0*, *B_Biosec_012=B_Biosec_012_0*, *B_Biosec=B_Biosec_0* and *B_cats=B_cats_yes* (factors are sorted from the most common).
## - low frequency for the factors *B_Biosecused=B_Biosecused_1*, *B_Biosec_012=B_Biosec_012_2*, *B_Biosec=B_Biosec_1* and *B_cats=B_cats_no* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by19* and *19*. :
## 
## - high frequency for the factors *B_Biosec_012=B_Biosec_012_2*, *B_Biosecused=B_Biosecused_1*, *B_Biosec=B_Biosec_1* and *B_cats=B_cats_no* (factors are sorted from the most common).
## - low frequency for the factors *B_Biosecused=B_Biosecused_0*, *B_Biosec_012=B_Biosec_012_0*, *B_Biosec=B_Biosec_0* and *B_cats=B_cats_yes* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by16* and *16*. :
## 
## - high frequency for the factors *B_Biosec_012=B_Biosec_012.NA*, *B_carcasstruckenter=B_carcasstruckenter_2*, *B_Entranceanimal=B_Entranceanimal_y* and *B_Entrancehuman=B_Entrancehuman_n* (factors are sorted from the most common).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Breeding management

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managbr.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 35
## $ MG_BR_giltpurchage_NUM_NO    <int> 4, 0, 0, 7, 3, 0, 0, 5, 7, 0, 0, ...
## $ MG_BR_giltchangebeforeins_NO <int> 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, ...
## $ MG_BR_giltflush_NO           <fctr> 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,...
## $ MG_BR_giltboarstart_NO       <fctr> 7, 6, 7,5, 7,5, 7, 7,5, 7, 7, 7,...
## $ MG_BR_giltinsage_NO          <fctr> 8, 7, 8, 7,5, 8, 9,5, 8, 8, 8, 8...
## $ MG_BR_heatgroup_NO           <fctr> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ MG_BR_heatdetec_startNUM_NO  <fctr> 0, 0, 5, 0, 1, 0, 3, 3, 0, 1, 3,...
## $ MG_BR_heatmarkback_NO        <fctr> 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,...
## $ MG_BR_artinspro_050_5099_100 <int> 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, ...
## $ MG_BR_farmsemenNUM_NO        <int> 0, 0, 95, 0, 50, 0, 0, 0, 0, 0, 0...
## $ MG_BR_insonceNUM_NO          <fctr> 0, 8, 0, 10, 0, 10, 80, 5, 0, 2,...
## $ MG_BR_once_012               <int> 0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 0, ...
## $ MG_BR_instriple_NO           <fctr> 1, 2, 10, 10, 15, 0, 0, 5, 0, 3,...
## $ MG_BR_triple_012             <fctr> 1, 1, 1, 1, 2, 0, 0, 1, 0, 1, 1,...
## $ MG_BR_nopregus               <fctr> 1, 1, 2, 1, 2, 0, 0, 1, 1, 1, 0,...
## $ MG_BR_bedtype_NO             <int> 0, 1, 12, 0, 0, 14, 1, 1, 0, 0, 1...
## $ MG_BR_bedny                  <int> 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, ...
## $ MG_BR_amount                 <int> 4, 2, 1, 0, 0, 1, 3, 2, 4, 4, 2, ...
## $ MG_BR_rootny                 <int> 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, ...
## $ MG_BR_toyny                  <fctr> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 4,...
## $ MG_BR_dirt_NUM_NO            <int> 30, 0, 0, 20, 40, 0, 20, 30, 10, ...
## $ MG_BR_animdirtmed            <int> 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, ...
## $ MG_BR_feedtype               <int> 4, 1, 25, 4, 4, 4, 4, 4, 4, 4, 4,...
## $ MG_BR_feedclean              <fctr> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,...
## $ MG_BR_calm                   <int> 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, ...
## $ MG_BR_dirtanim_NUM_NO        <int> 20, 0, 10, 10, NA, 20, 30, 30, 10...
## $ MG_BR_dirtanimmed            <int> 2, 1, 1, 1, NA, 2, 2, 2, 1, NA, 2...
## $ MG_BR_ster                   <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_BR_sowsperboar_NUM_NO     <fctr> 150, 37, 75, 92, 525, 30, 115, 2...
## $ OUT_SOW_mort_proNUM          <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6,...
## $ OUT_SOW_mort_dic             <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, ...
## $ OUT_SOW_totremproNUM         <int> 34, 38, 53, 57, 65, 64, 47, 44, 2...
## $ OUT_SOW_totrem_dic           <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM           <int> 29, 33, 45, 30, 55, 64, 30, 31, 2...
## $ OUT_SOW_cull_dic             <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34]  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "MG_BR_once_012"    "MG_BR_animdirtmed" "MG_BR_dirtanimmed"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="yellow") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
MG_BR_giltpurchage_NUM_NO (mean (sd)) 3.00 (2.63) 2.40 (2.26) 0.430
MG_BR_heatdetec_startNUM_NO (mean (sd)) 3.35 (2.44) 3.55 (2.37) 0.785
MG_BR_farmsemenNUM_NO (mean (sd)) 1.96 (2.06) 2.00 (1.97) 0.944
MG_BR_insonceNUM_NO (mean (sd)) 4.04 (3.75) 6.15 (3.99) 0.082
MG_BR_dirt_NUM_NO (mean (sd)) 2.27 (1.52) 2.59 (1.54) 0.527
MG_BR_dirtanim_NUM_NO (mean (sd)) 2.52 (1.54) 2.79 (1.23) 0.552
MG_BR_sowsperboar_NUM_NO (mean (sd)) 19.65 (10.08) 17.55 (11.15) 0.520
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
MG_BR_giltchangebeforeins_NO = 1 (%) 9 (39.1) 10 ( 50.0) 0.683
MG_BR_giltflush_NO (%) 0.299
0 13 (56.5) 10 ( 50.0)
1 8 (34.8) 10 ( 50.0)
noneed 2 ( 8.7) 0 ( 0.0)
MG_BR_giltboarstart_NO (%) 0.158
0 4 (17.4) 0 ( 0.0)
3 1 ( 4.3) 0 ( 0.0)
4 0 ( 0.0) 1 ( 5.0)
6 4 (17.4) 4 ( 20.0)
6,5 3 (13.0) 0 ( 0.0)
7 6 (26.1) 10 ( 50.0)
7,5 4 (17.4) 4 ( 20.0)
8 1 ( 4.3) 0 ( 0.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_giltinsage_NO (%) 0.139
0 3 (13.0) 0 ( 0.0)
2ndheat 0 ( 0.0) 1 ( 5.0)
6 0 ( 0.0) 1 ( 5.0)
7 2 ( 8.7) 0 ( 0.0)
7,5 1 ( 4.3) 1 ( 5.0)
8 14 (60.9) 11 ( 55.0)
8,5 0 ( 0.0) 4 ( 20.0)
9 1 ( 4.3) 0 ( 0.0)
9,5 2 ( 8.7) 1 ( 5.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_heatgroup_NO (%) 0.361
0 5 (21.7) 4 ( 20.0)
1 17 (73.9) 14 ( 70.0)
no 1 ( 4.3) 0 ( 0.0)
noinfo 0 ( 0.0) 2 ( 10.0)
MG_BR_heatmarkback_NO (%) 0.211
0 8 (34.8) 3 ( 15.0)
1 15 (65.2) 16 ( 80.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_artinspro_050_5099_100 (%) 0.785
0 1 ( 4.3) 1 ( 5.0)
1 8 (34.8) 5 ( 25.0)
2 14 (60.9) 14 ( 70.0)
MG_BR_once_012 (%) 0.059
0 10 (45.5) 5 ( 26.3)
1 12 (54.5) 10 ( 52.6)
2 0 ( 0.0) 4 ( 21.1)
MG_BR_instriple_NO (%) 0.630
0 7 (30.4) 4 ( 20.0)
1 1 ( 4.3) 2 ( 10.0)
10 5 (21.7) 6 ( 30.0)
15 0 ( 0.0) 2 ( 10.0)
2 1 ( 4.3) 0 ( 0.0)
3 1 ( 4.3) 1 ( 5.0)
30 1 ( 4.3) 0 ( 0.0)
33 0 ( 0.0) 1 ( 5.0)
5 6 (26.1) 3 ( 15.0)
noinfo 1 ( 4.3) 1 ( 5.0)
MG_BR_triple_012 (%) 0.621
0 7 (30.4) 4 ( 20.0)
1 14 (60.9) 12 ( 60.0)
2 1 ( 4.3) 3 ( 15.0)
noinfo 1 ( 4.3) 1 ( 5.0)
MG_BR_nopregus (%) 0.179
0 10 (43.5) 3 ( 15.0)
1 10 (43.5) 12 ( 60.0)
2 3 (13.0) 4 ( 20.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_bedtype_NO (%) 0.229
0 7 (30.4) 11 ( 55.0)
1 7 (30.4) 4 ( 20.0)
2 1 ( 4.3) 4 ( 20.0)
5 1 ( 4.3) 0 ( 0.0)
12 3 (13.0) 1 ( 5.0)
14 2 ( 8.7) 0 ( 0.0)
25 1 ( 4.3) 0 ( 0.0)
125 1 ( 4.3) 0 ( 0.0)
MG_BR_bedny = 1 (%) 16 (69.6) 9 ( 45.0) 0.187
MG_BR_amount (%) 0.393
0 2 ( 8.7) 5 ( 25.0)
1 5 (21.7) 1 ( 5.0)
2 3 (13.0) 3 ( 15.0)
3 7 (30.4) 5 ( 25.0)
4 6 (26.1) 6 ( 30.0)
MG_BR_rootny = 1 (%) 17 (73.9) 8 ( 40.0) 0.053
MG_BR_toyny (%) 0.090
0 16 (69.6) 8 ( 40.0)
1 6 (26.1) 10 ( 50.0)
4 1 ( 4.3) 0 ( 0.0)
y 0 ( 0.0) 2 ( 10.0)
MG_BR_animdirtmed = 2 (%) 8 (36.4) 9 ( 52.9) 0.478
MG_BR_feedtype (%) 0.145
1 1 ( 4.3) 0 ( 0.0)
2 0 ( 0.0) 2 ( 10.0)
3 3 (13.0) 0 ( 0.0)
4 18 (78.3) 18 ( 90.0)
25 1 ( 4.3) 0 ( 0.0)
MG_BR_feedclean (%) 0.246
0 20 (87.0) 15 ( 75.0)
1 2 ( 8.7) 5 ( 25.0)
no 1 ( 4.3) 0 ( 0.0)
MG_BR_calm (%) 0.506
0 1 ( 4.3) 0 ( 0.0)
1 21 (91.3) 18 ( 90.0)
2 1 ( 4.3) 2 ( 10.0)
MG_BR_dirtanimmed = 2 (%) 9 (42.9) 10 ( 52.6) 0.763
MG_BR_ster = 1 (%) 4 (17.4) 2 ( 10.0) 0.798
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
MG_BR_giltpurchage_NUM_NO (mean (sd)) 3.45 (2.74) 1.95 (1.88) 0.043
MG_BR_heatdetec_startNUM_NO (mean (sd)) 2.91 (2.20) 4.00 (2.49) 0.135
MG_BR_farmsemenNUM_NO (mean (sd)) 1.36 (1.05) 2.62 (2.52) 0.037
MG_BR_insonceNUM_NO (mean (sd)) 5.86 (4.16) 4.14 (3.64) 0.157
MG_BR_dirt_NUM_NO (mean (sd)) 2.26 (1.19) 2.55 (1.79) 0.562
MG_BR_dirtanim_NUM_NO (mean (sd)) 2.80 (1.28) 2.50 (1.50) 0.501
MG_BR_sowsperboar_NUM_NO (mean (sd)) 18.41 (10.39) 18.95 (10.89) 0.868
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
MG_BR_giltchangebeforeins_NO = 1 (%) 8 (36.4) 11 ( 52.4) 0.453
MG_BR_giltflush_NO (%) 0.128
0 15 (68.2) 8 ( 38.1)
1 6 (27.3) 12 ( 57.1)
noneed 1 ( 4.5) 1 ( 4.8)
MG_BR_giltboarstart_NO (%) 0.530
0 2 ( 9.1) 2 ( 9.5)
3 1 ( 4.5) 0 ( 0.0)
4 1 ( 4.5) 0 ( 0.0)
6 5 (22.7) 3 ( 14.3)
6,5 1 ( 4.5) 2 ( 9.5)
7 9 (40.9) 7 ( 33.3)
7,5 2 ( 9.1) 6 ( 28.6)
8 1 ( 4.5) 0 ( 0.0)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_giltinsage_NO (%) 0.771
0 2 ( 9.1) 1 ( 4.8)
2ndheat 1 ( 4.5) 0 ( 0.0)
6 1 ( 4.5) 0 ( 0.0)
7 1 ( 4.5) 1 ( 4.8)
7,5 1 ( 4.5) 1 ( 4.8)
8 12 (54.5) 13 ( 61.9)
8,5 1 ( 4.5) 3 ( 14.3)
9 1 ( 4.5) 0 ( 0.0)
9,5 2 ( 9.1) 1 ( 4.8)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_heatgroup_NO (%) 0.426
0 3 (13.6) 6 ( 28.6)
1 18 (81.8) 13 ( 61.9)
no 0 ( 0.0) 1 ( 4.8)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_BR_heatmarkback_NO (%) 0.044
0 9 (40.9) 2 ( 9.5)
1 13 (59.1) 18 ( 85.7)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_artinspro_050_5099_100 (%) 0.666
0 1 ( 4.5) 1 ( 4.8)
1 8 (36.4) 5 ( 23.8)
2 13 (59.1) 15 ( 71.4)
MG_BR_once_012 (%) 0.894
0 7 (33.3) 8 ( 40.0)
1 12 (57.1) 10 ( 50.0)
2 2 ( 9.5) 2 ( 10.0)
MG_BR_instriple_NO (%) 0.432
0 7 (31.8) 4 ( 19.0)
1 2 ( 9.1) 1 ( 4.8)
10 4 (18.2) 7 ( 33.3)
15 0 ( 0.0) 2 ( 9.5)
2 1 ( 4.5) 0 ( 0.0)
3 2 ( 9.1) 0 ( 0.0)
30 0 ( 0.0) 1 ( 4.8)
33 0 ( 0.0) 1 ( 4.8)
5 5 (22.7) 4 ( 19.0)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_BR_triple_012 (%) 0.175
0 7 (31.8) 4 ( 19.0)
1 14 (63.6) 12 ( 57.1)
2 0 ( 0.0) 4 ( 19.0)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_BR_nopregus (%) 0.099
0 7 (31.8) 6 ( 28.6)
1 14 (63.6) 8 ( 38.1)
2 1 ( 4.5) 6 ( 28.6)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_bedtype_NO (%) 0.529
0 8 (36.4) 10 ( 47.6)
1 6 (27.3) 5 ( 23.8)
2 4 (18.2) 1 ( 4.8)
5 0 ( 0.0) 1 ( 4.8)
12 1 ( 4.5) 3 ( 14.3)
14 1 ( 4.5) 1 ( 4.8)
25 1 ( 4.5) 0 ( 0.0)
125 1 ( 4.5) 0 ( 0.0)
MG_BR_bedny = 1 (%) 14 (63.6) 11 ( 52.4) 0.661
MG_BR_amount (%) 0.294
0 2 ( 9.1) 5 ( 23.8)
1 2 ( 9.1) 4 ( 19.0)
2 5 (22.7) 1 ( 4.8)
3 7 (31.8) 5 ( 23.8)
4 6 (27.3) 6 ( 28.6)
MG_BR_rootny = 1 (%) 14 (63.6) 11 ( 52.4) 0.661
MG_BR_toyny (%) 0.450
0 14 (63.6) 10 ( 47.6)
1 6 (27.3) 10 ( 47.6)
4 1 ( 4.5) 0 ( 0.0)
y 1 ( 4.5) 1 ( 4.8)
MG_BR_animdirtmed = 2 (%) 8 (42.1) 9 ( 45.0) 1.000
MG_BR_feedtype (%) 0.115
1 1 ( 4.5) 0 ( 0.0)
2 2 ( 9.1) 0 ( 0.0)
3 3 (13.6) 0 ( 0.0)
4 16 (72.7) 20 ( 95.2)
25 0 ( 0.0) 1 ( 4.8)
MG_BR_feedclean (%) 0.563
0 18 (81.8) 17 ( 81.0)
1 3 (13.6) 4 ( 19.0)
no 1 ( 4.5) 0 ( 0.0)
MG_BR_calm (%) 0.513
0 0 ( 0.0) 1 ( 4.8)
1 20 (90.9) 19 ( 90.5)
2 2 ( 9.1) 1 ( 4.8)
MG_BR_dirtanimmed = 2 (%) 11 (55.0) 8 ( 40.0) 0.527
MG_BR_ster = 1 (%) 2 ( 9.1) 4 ( 19.0) 0.616
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(18,19),quali.sup=c(16:17), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(18, 19), quali.sup = c(16:17),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.295   0.205   0.185   0.161   0.145   0.134
## % of var.             13.013   9.060   8.162   7.107   6.387   5.933
## Cumulative % of var.  13.013  22.073  30.234  37.341  43.728  49.661
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.110   0.104   0.099   0.095   0.088   0.078
## % of var.              4.873   4.605   4.376   4.182   3.884   3.452
## Cumulative % of var.  54.534  59.139  63.515  67.698  71.581  75.034
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.069   0.059   0.056   0.050   0.047   0.044
## % of var.              3.024   2.615   2.481   2.212   2.062   1.955
## Cumulative % of var.  78.058  80.673  83.154  85.366  87.428  89.383
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.038   0.035   0.030   0.028   0.021   0.020
## % of var.              1.682   1.533   1.337   1.217   0.944   0.865
## Cumulative % of var.  91.065  92.598  93.935  95.152  96.097  96.962
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.019   0.013   0.011   0.009   0.006   0.004
## % of var.              0.821   0.564   0.480   0.414   0.274   0.183
## Cumulative % of var.  97.783  98.347  98.827  99.240  99.515  99.698
##                       Dim.31  Dim.32  Dim.33  Dim.34
## Variance               0.004   0.002   0.001   0.000
## % of var.              0.157   0.091   0.055   0.000
## Cumulative % of var.  99.855  99.945 100.000 100.000
## 
## Individuals (the 10 first)
##                                   Dim.1    ctr   cos2    Dim.2    ctr
## 1                              |  0.462  1.682  0.090 | -0.792  7.099
## 2                              | -0.654  3.372  0.109 |  0.231  0.603
## 3                              | -0.511  2.056  0.063 |  0.265  0.797
## 4                              |  0.372  1.091  0.128 | -0.389  1.714
## 5                              |  0.870  5.966  0.234 | -0.272  0.835
## 6                              | -0.688  3.727  0.343 |  0.145  0.238
## 7                              | -0.241  0.457  0.021 | -0.470  2.502
## 8                              |  0.145  0.165  0.014 |  0.207  0.485
## 9                              |  0.105  0.087  0.009 | -0.136  0.209
## 10                             |  0.497  1.951  0.145 | -0.459  2.384
##                                  cos2    Dim.3    ctr   cos2  
## 1                               0.265 |  0.240  0.726  0.024 |
## 2                               0.014 |  0.116  0.171  0.003 |
## 3                               0.017 | -0.604  4.586  0.088 |
## 4                               0.140 | -0.116  0.168  0.012 |
## 5                               0.023 | -0.415  2.166  0.053 |
## 6                               0.015 |  0.116  0.170  0.010 |
## 7                               0.082 |  0.452  2.564  0.075 |
## 8                               0.029 | -0.210  0.553  0.030 |
## 9                               0.016 |  0.271  0.926  0.062 |
## 10                              0.123 | -0.103  0.134  0.006 |
## 
## Categories (the 10 first)
##                                   Dim.1    ctr   cos2 v.test    Dim.2
## MG_BR_artinspro_050_5099_100_0 | -1.230  1.589  0.074 -1.760 |  0.305
## MG_BR_artinspro_050_5099_100_1 | -0.685  3.206  0.203 -2.922 | -0.059
## MG_BR_artinspro_050_5099_100_2 |  0.406  2.424  0.307  3.593 |  0.006
## MG_BR_once_012_0               |  0.198  0.308  0.021  0.938 | -0.164
## MG_BR_once_012_1               | -0.244  0.688  0.062 -1.618 | -0.148
## MG_BR_once_012_2               | -0.536  0.603  0.029 -1.112 | -0.274
## MG_BR_once_012_Not Assigned    |  2.271  5.422  0.252  3.250 |  3.412
## MG_BR_triple_012_0             | -0.531  1.631  0.097 -2.018 | -0.087
## MG_BR_triple_012_1             | -0.018  0.004  0.000 -0.144 | -0.151
## MG_BR_triple_012_2             |  0.442  0.411  0.020  0.917 | -0.486
##                                   ctr   cos2 v.test    Dim.3    ctr   cos2
## MG_BR_artinspro_050_5099_100_0  0.141  0.005  0.437 |  2.238  8.393  0.244
## MG_BR_artinspro_050_5099_100_1  0.035  0.002 -0.253 |  0.576  3.617  0.144
## MG_BR_artinspro_050_5099_100_2  0.001  0.000  0.051 | -0.427  4.285  0.341
## MG_BR_once_012_0                0.305  0.014 -0.778 | -0.588  4.350  0.185
## MG_BR_once_012_1                0.366  0.023 -0.984 |  0.115  0.245  0.014
## MG_BR_once_012_2                0.227  0.008 -0.570 |  1.247  5.211  0.159
## MG_BR_once_012_Not Assigned    17.575  0.568  4.883 |  0.651  0.709  0.021
## MG_BR_triple_012_0              0.063  0.003 -0.330 |  0.707  4.609  0.172
## MG_BR_triple_012_1              0.447  0.035 -1.210 | -0.371  2.991  0.210
## MG_BR_triple_012_2              0.713  0.024 -1.008 |  0.139  0.064  0.002
##                                v.test  
## MG_BR_artinspro_050_5099_100_0  3.203 |
## MG_BR_artinspro_050_5099_100_1  2.458 |
## MG_BR_artinspro_050_5099_100_2 -3.784 |
## MG_BR_once_012_0               -2.790 |
## MG_BR_once_012_1                0.764 |
## MG_BR_once_012_2                2.588 |
## MG_BR_once_012_Not Assigned     0.931 |
## MG_BR_triple_012_0              2.687 |
## MG_BR_triple_012_1             -2.970 |
## MG_BR_triple_012_2              0.288 |
## 
## Categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## MG_BR_artinspro_050_5099_100   | 0.319 0.005 0.452 |
## MG_BR_once_012                 | 0.311 0.569 0.292 |
## MG_BR_triple_012               | 0.330 0.579 0.232 |
## MG_BR_nopregus                 | 0.477 0.441 0.112 |
## MG_BR_bedny                    | 0.696 0.072 0.021 |
## MG_BR_amount                   | 0.699 0.132 0.132 |
## MG_BR_rootny                   | 0.439 0.136 0.021 |
## MG_BR_toyny                    | 0.197 0.125 0.487 |
## MG_BR_animdirtmed              | 0.129 0.254 0.141 |
## MG_BR_feedtype                 | 0.232 0.035 0.448 |
## 
## Supplementary categories
##                                   Dim.1   cos2 v.test    Dim.2   cos2
## OUT_SOW_totrem_dic_0           | -0.301  0.087 -1.909 |  0.166  0.026
## OUT_SOW_totrem_dic_1           |  0.288  0.087  1.909 | -0.158  0.026
## OUT_SOW_cull_dic_0             | -0.161  0.027 -1.067 |  0.007  0.000
## OUT_SOW_cull_dic_1             |  0.169  0.027  1.067 | -0.007  0.000
##                                v.test    Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0            1.051 | -0.131  0.016 -0.828 |
## OUT_SOW_totrem_dic_1           -1.051 |  0.125  0.016  0.828 |
## OUT_SOW_cull_dic_0              0.046 |  0.076  0.006  0.502 |
## OUT_SOW_cull_dic_1             -0.046 | -0.079  0.006 -0.502 |
## 
## Supplementary categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic             | 0.087 0.026 0.016 |
## OUT_SOW_cull_dic               | 0.027 0.000 0.006 |
## 
## Supplementary continuous variables
##                                   Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM            |  0.348 | -0.003 |  0.289 |
## OUT_SOW_cullproNUM             |  0.232 | -0.045 |  0.051 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("24", "21", "27", "39", "23", "32", "6", "1", "35", "13")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by6* and *6*. :
## 
## - high frequency for the factors *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_BR_amount=MG_BR_amount_3*, *MG_BR_amount=MG_BR_amount_2*, *MG_BR_amount=MG_BR_amount_1* and *MG_BR_ster=MG_BR_ster_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_amount=MG_BR_amount_4*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_BR_feedtype=MG_BR_feedtype_4*, *MG_BR_amount=MG_BR_amount_0*, *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_2* and *MG_BR_ster=MG_BR_ster_1* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by1* and *1*. :
## 
## - high frequency for the factors *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_BR_amount=MG_BR_amount_4*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_BR_amount=MG_BR_amount_0*, *MG_BR_ster=MG_BR_ster_1*, *MG_BR_feedtype=MG_BR_feedtype_4* and *MG_BR_dirtanimmed=MG_BR_dirtanimmed_Not Assigned* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_BR_amount=MG_BR_amount_3*, *MG_BR_ster=MG_BR_ster_0*, *MG_BR_amount=MG_BR_amount_1* and *MG_BR_amount=MG_BR_amount_2* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by21* and *21*. :
## 
## - high frequency for the factors *MG_BR_triple_012=MG_BR_triple_012_noinfo*, *MG_BR_once_012=MG_BR_once_012_Not Assigned*, *MG_BR_feedclean=MG_BR_feedclean_1* and *MG_BR_nopregus=MG_BR_nopregus_noinfo* (factors are sorted from the most common).
## - low frequency for the factor **.
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Gestation management

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managpr.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 19
## $ MG_PR_earlyHAR_kaNUM      <fctr> 0,9, 0,15, 0, 0,2, 1, 0,05, 0,2, 0,...
## $ MG_PR_type                <int> 2, 1, 12, 13, 2, 1, 2, 1, 14, 1, 13,...
## $ MG_PR_rootyn              <int> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_toyyn               <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...
## $ MG_PR_toy                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, ...
## $ MG_PR_kuivaliete          <int> 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, ...
## $ MG_PR_ruok_0nonlock_1lock <int> 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...
## $ MG_PR_feedtype            <int> 3, 1, 25, 4, 5, 4, 4, 5, 4, 4, 4, 5,...
## $ MG_PR_calm                <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_dirt_NUM_NO         <int> 20, 0, 10, 20, 20, 20, 20, 10, 20, 1...
## $ MG_PR_animdirtmed         <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, ...
## $ MG_PR_ster                <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...
## $ MG_PR_late_HAR_kaNUM_NO   <fctr> 0,9, 0,15, 0, 0,1, , , 0,2, 0,6, 0,...
## $ OUT_SOW_mort_proNUM       <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9,...
## $ OUT_SOW_mort_dic          <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, ...
## $ OUT_SOW_totremproNUM      <int> 34, 38, 53, 57, 65, 64, 47, 44, 24, ...
## $ OUT_SOW_totrem_dic        <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM        <int> 29, 33, 45, 30, 55, 64, 30, 31, 24, ...
## $ OUT_SOW_cull_dic          <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="grey") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
MG_PR_dirt_NUM_NO (mean (sd)) 3.00 (1.95) 4.26 (2.38) 0.066
MG_PR_late_HAR_kaNUM_NO (mean (sd)) 4.87 (4.70) 5.95 (5.57) 0.494
MG_PR_earlyHAR_kaNUM (mean (sd)) 7.91 (4.73) 10.15 (5.41) 0.156
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
MG_PR_type (%) 0.295
1 8 (34.8) 4 ( 20.0)
2 8 (34.8) 10 ( 50.0)
3 0 ( 0.0) 1 ( 5.0)
12 3 (13.0) 0 ( 0.0)
13 3 (13.0) 2 ( 10.0)
14 1 ( 4.3) 0 ( 0.0)
23 0 ( 0.0) 1 ( 5.0)
123 0 ( 0.0) 1 ( 5.0)
124 0 ( 0.0) 1 ( 5.0)
MG_PR_rootyn = 1 (%) 20 (87.0) 16 ( 80.0) 0.840
MG_PR_toyyn = 1 (%) 7 (30.4) 9 ( 45.0) 0.503
MG_PR_toy (%) 0.270
0 16 (69.6) 11 ( 55.0)
1 0 ( 0.0) 1 ( 5.0)
2 3 (13.0) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
4 4 (17.4) 4 ( 20.0)
5 0 ( 0.0) 1 ( 5.0)
14 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
MG_PR_kuivaliete (%) 0.133
1 7 (30.4) 6 ( 30.0)
2 12 (52.2) 14 ( 70.0)
12 4 (17.4) 0 ( 0.0)
MG_PR_ruok_0nonlock_1lock (%) 0.248
0 10 (43.5) 12 ( 60.0)
1 13 (56.5) 7 ( 35.0)
3 0 ( 0.0) 1 ( 5.0)
MG_PR_feedtype (%) 0.354
1 2 ( 8.7) 1 ( 5.0)
2 1 ( 4.3) 4 ( 20.0)
3 4 (17.4) 4 ( 20.0)
4 13 (56.5) 7 ( 35.0)
5 1 ( 4.3) 3 ( 15.0)
6 0 ( 0.0) 1 ( 5.0)
25 1 ( 4.3) 0 ( 0.0)
34 1 ( 4.3) 0 ( 0.0)
MG_PR_calm = 2 (%) 1 ( 4.3) 2 ( 10.0) 0.900
MG_PR_animdirtmed = 2 (%) 6 (26.1) 9 ( 47.4) 0.267
MG_PR_ster = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
MG_PR_dirt_NUM_NO (mean (sd)) 3.33 (1.93) 3.81 (2.50) 0.494
MG_PR_late_HAR_kaNUM_NO (mean (sd)) 6.41 (4.96) 4.29 (5.11) 0.174
MG_PR_earlyHAR_kaNUM (mean (sd)) 8.59 (4.56) 9.33 (5.74) 0.640
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
MG_PR_type (%) 0.640
1 5 (22.7) 7 ( 33.3)
2 10 (45.5) 8 ( 38.1)
3 0 ( 0.0) 1 ( 4.8)
12 1 ( 4.5) 2 ( 9.5)
13 3 (13.6) 2 ( 9.5)
14 1 ( 4.5) 0 ( 0.0)
23 1 ( 4.5) 0 ( 0.0)
123 0 ( 0.0) 1 ( 4.8)
124 1 ( 4.5) 0 ( 0.0)
MG_PR_rootyn = 1 (%) 20 (90.9) 16 ( 76.2) 0.372
MG_PR_toyyn = 1 (%) 6 (27.3) 10 ( 47.6) 0.287
MG_PR_toy (%) 0.456
0 16 (72.7) 11 ( 52.4)
1 0 ( 0.0) 1 ( 4.8)
2 1 ( 4.5) 2 ( 9.5)
3 0 ( 0.0) 1 ( 4.8)
4 3 (13.6) 5 ( 23.8)
5 1 ( 4.5) 0 ( 0.0)
14 0 ( 0.0) 1 ( 4.8)
24 1 ( 4.5) 0 ( 0.0)
MG_PR_kuivaliete (%) 0.663
1 8 (36.4) 5 ( 23.8)
2 12 (54.5) 14 ( 66.7)
12 2 ( 9.1) 2 ( 9.5)
MG_PR_ruok_0nonlock_1lock (%) 0.560
0 12 (54.5) 10 ( 47.6)
1 10 (45.5) 10 ( 47.6)
3 0 ( 0.0) 1 ( 4.8)
MG_PR_feedtype (%) 0.646
1 1 ( 4.5) 2 ( 9.5)
2 4 (18.2) 1 ( 4.8)
3 4 (18.2) 4 ( 19.0)
4 10 (45.5) 10 ( 47.6)
5 2 ( 9.1) 2 ( 9.5)
6 0 ( 0.0) 1 ( 4.8)
25 0 ( 0.0) 1 ( 4.8)
34 1 ( 4.5) 0 ( 0.0)
MG_PR_calm = 2 (%) 0 ( 0.0) 3 ( 14.3) 0.215
MG_PR_animdirtmed = 2 (%) 6 (28.6) 9 ( 42.9) 0.520
MG_PR_ster = 1 (%) 2 ( 9.1) 2 ( 9.5) 1.000
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(14,15),quali.sup=c(12:13), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(14, 15), quali.sup = c(12:13),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.351   0.274   0.237   0.218   0.211   0.201
## % of var.             11.689   9.130   7.898   7.281   7.034   6.704
## Cumulative % of var.  11.689  20.819  28.718  35.998  43.032  49.737
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.172   0.164   0.157   0.122   0.119   0.112
## % of var.              5.749   5.459   5.238   4.078   3.957   3.741
## Cumulative % of var.  55.486  60.945  66.183  70.261  74.218  77.958
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.100   0.093   0.091   0.080   0.060   0.053
## % of var.              3.347   3.101   3.030   2.654   1.990   1.765
## Cumulative % of var.  81.306  84.406  87.437  90.091  92.081  93.846
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.043   0.036   0.032   0.026   0.015   0.013
## % of var.              1.427   1.214   1.055   0.866   0.485   0.418
## Cumulative % of var.  95.273  96.486  97.541  98.407  98.893  99.311
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.011   0.010   0.000   0.000   0.000   0.000
## % of var.              0.369   0.320   0.000   0.000   0.000   0.000
## Cumulative % of var.  99.680 100.000 100.000 100.000 100.000 100.000
##                       Dim.31  Dim.32  Dim.33
## Variance               0.000   0.000   0.000
## % of var.              0.000   0.000   0.000
## Cumulative % of var. 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                         Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                    |  0.089  0.052  0.008 | -0.061  0.032  0.004 |
## 2                    | -0.526  1.832  0.137 |  0.335  0.953  0.056 |
## 3                    | -0.702  3.272  0.088 |  0.358  1.086  0.023 |
## 4                    | -0.263  0.460  0.041 | -0.433  1.595  0.110 |
## 5                    |  0.155  0.160  0.017 |  0.390  1.294  0.104 |
## 6                    | -0.741  3.637  0.591 |  0.015  0.002  0.000 |
## 7                    | -0.207  0.284  0.062 | -0.142  0.170  0.029 |
## 8                    | -0.250  0.414  0.036 |  0.583  2.881  0.198 |
## 9                    | -0.606  2.436  0.084 | -0.203  0.351  0.009 |
## 10                   | -0.741  3.637  0.591 |  0.015  0.002  0.000 |
##                       Dim.3    ctr   cos2  
## 1                    -0.040  0.015  0.002 |
## 2                    -0.367  1.319  0.067 |
## 3                    -0.142  0.199  0.004 |
## 4                     0.397  1.546  0.092 |
## 5                     0.274  0.736  0.051 |
## 6                     0.005  0.000  0.000 |
## 7                     0.240  0.564  0.083 |
## 8                     0.047  0.022  0.001 |
## 9                     0.401  1.575  0.037 |
## 10                    0.005  0.000  0.000 |
## 
## Categories (the 10 first)
##                         Dim.1    ctr   cos2 v.test    Dim.2    ctr   cos2
## MG_PR_type_1         | -0.776  4.354  0.233 -3.128 |  0.324  0.974  0.041
## MG_PR_type_12        | -1.067  2.061  0.085 -1.895 |  0.259  0.155  0.005
## MG_PR_type_123       |  2.047  2.526  0.100  2.047 |  3.754 10.877  0.336
## MG_PR_type_124       |  1.404  1.189  0.047  1.404 |  1.063  0.873  0.027
## MG_PR_type_13        | -0.416  0.522  0.023 -0.978 | -0.667  1.718  0.059
## MG_PR_type_14        | -1.023  0.632  0.025 -1.023 | -0.388  0.116  0.004
## MG_PR_type_2         |  0.741  5.954  0.395  4.073 | -0.240  0.802  0.042
## MG_PR_type_23        | -0.987  0.588  0.023 -0.987 | -0.559  0.241  0.007
## MG_PR_type_3         | -0.181  0.020  0.001 -0.181 | -0.878  0.595  0.018
## MG_PR_rootyn_0       |  0.661  1.847  0.085  1.890 | -1.050  5.952  0.214
##                      v.test    Dim.3    ctr   cos2 v.test  
## MG_PR_type_1          1.308 | -0.515  2.841  0.103 -2.077 |
## MG_PR_type_12         0.460 |  0.033  0.003  0.000  0.059 |
## MG_PR_type_123        3.754 |  3.481 10.810  0.288  3.481 |
## MG_PR_type_124        1.063 | -1.063  1.008  0.027 -1.063 |
## MG_PR_type_13        -1.569 |  0.331  0.488  0.014  0.777 |
## MG_PR_type_14        -0.388 |  0.823  0.604  0.016  0.823 |
## MG_PR_type_2         -1.321 | -0.079  0.101  0.005 -0.436 |
## MG_PR_type_23        -0.559 |  1.921  3.293  0.088  1.921 |
## MG_PR_type_3         -0.878 |  0.692  0.427  0.011  0.692 |
## MG_PR_rootyn_0       -2.999 |  0.361  0.813  0.025  1.031 |
## 
## Categorical variables (eta2)
##                             Dim.1 Dim.2 Dim.3  
## MG_PR_type                | 0.688 0.493 0.510 |
## MG_PR_rootyn              | 0.085 0.214 0.025 |
## MG_PR_toyyn               | 0.487 0.006 0.062 |
## MG_PR_toy                 | 0.621 0.762 0.740 |
## MG_PR_kuivaliete          | 0.275 0.069 0.132 |
## MG_PR_ruok_0nonlock_1lock | 0.458 0.538 0.132 |
## MG_PR_feedtype            | 0.613 0.556 0.656 |
## MG_PR_calm                | 0.093 0.156 0.020 |
## MG_PR_animdirtmed         | 0.160 0.008 0.192 |
## MG_PR_ster                | 0.202 0.199 0.137 |
## 
## Supplementary categories
##                         Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## OUT_SOW_totrem_dic_0 | -0.343  0.112 -2.173 |  0.071  0.005  0.451 |
## OUT_SOW_totrem_dic_1 |  0.328  0.112  2.173 | -0.068  0.005 -0.451 |
## OUT_SOW_cull_dic_0   | -0.145  0.022 -0.962 |  0.074  0.006  0.493 |
## OUT_SOW_cull_dic_1   |  0.152  0.022  0.962 | -0.078  0.006 -0.493 |
##                       Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0 -0.038  0.001 -0.240 |
## OUT_SOW_totrem_dic_1  0.036  0.001  0.240 |
## OUT_SOW_cull_dic_0    0.026  0.001  0.172 |
## OUT_SOW_cull_dic_1   -0.027  0.001 -0.172 |
## 
## Supplementary categorical variables (eta2)
##                        Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic   | 0.112 0.005 0.001 |
## OUT_SOW_cull_dic     | 0.022 0.006 0.001 |
## 
## Supplementary continuous variables
##                         Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM  |  0.307 |  0.043 | -0.070 |
## OUT_SOW_cullproNUM   |  0.240 | -0.071 |  0.029 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("36", "10", "6", "20", "22", "29", "13", "27", "42", "32")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 7 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by6* and *6*. :
## 
## - high frequency for the factors *MG_PR_type=MG_PR_type_1*, *MG_PR_toy=MG_PR_toy_0*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_1*, *MG_PR_animdirtmed=MG_PR_animdirtmed_1*, *OUT_SOW_mort_dic=OUT_SOW_mort_dic_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* and *MG_PR_type=MG_PR_type_12* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_PR_kuivaliete=MG_PR_kuivaliete_2*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2*, *OUT_SOW_mort_dic=OUT_SOW_mort_dic_1*, *MG_PR_toy=MG_PR_toy_4*, *MG_PR_feedtype=MG_PR_feedtype_3* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals sharing :
## 
## - high frequency for the factors *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_type=MG_PR_type_13* and *MG_PR_kuivaliete=MG_PR_kuivaliete_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_1* and *MG_PR_type=MG_PR_type_1* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals sharing :
## 
## - high frequency for the factors *MG_PR_feedtype=MG_PR_feedtype_6* and *MG_PR_toy=MG_PR_toy_3* (factors are sorted from the most common).
## 
## The cluster 4 is made of individuals such as*. This group is characterized by13* and *13*. :
## 
## - high frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_feedtype=MG_PR_feedtype_3*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2* and *MG_PR_feedtype=MG_PR_feedtype_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_animdirtmed=MG_PR_animdirtmed_1* and *MG_PR_type=MG_PR_type_1* (factors are sorted from the rarest).
## 
## The cluster 5 is made of individuals sharing :
## 
## - high frequency for the factors *MG_PR_toy=MG_PR_toy_24* and *MG_PR_type=MG_PR_type_124* (factors are sorted from the most common).
## 
## The 1st cluster is made of individuals such as *29*. This group is characterized by :
## 
## - high frequency for the factors *MG_PR_toy=MG_PR_toy_1* and *MG_PR_type=MG_PR_type_123* (factors are sorted from the most common).
## 
## The 1st cluster is made of individuals such as *36*. This group is characterized by :
## 
## - high frequency for the factors *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_3* and *MG_PR_toy=MG_PR_toy_14* (factors are sorted from the most common).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Farrowing management

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="managfar.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 31
## $ MG_FAR_ToFarunitNUM_NO          <int> 6, 5, 4, 7, 7, 7, 3, 5, 5, 5, ...
## $ MG_FAR_ind_0no_1rout_2sometimes <int> 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, ...
## $ MG_FAR_NestmatdaysNUM_NO        <int> 6, 1, 4, 7, 2, 0, 3, 2, 5, 3, ...
## $ MG_FAR_nestmatamount            <int> 3, 2, 2, 2, 3, 2, 3, 1, 2, 2, ...
## $ MG_FAR_nestmat_NO               <fctr> STR, STR, STR_CUT, heiina , _...
## $ MG_FAR_ox_0_13_46_7             <int> 3, 1, 1, 1, 3, 3, 3, 2, 2, 2, ...
## $ MG_FAR_obstex_preox             <fctr> 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,...
## $ MG_FAR_far_assist_CAT           <fctr> 50, 20-50, <6, 20-50, noinfo,...
## $ MG_FAR_farassist_MAY_NO         <fctr> WASH_GLO_LUBR, WASH_HANDWASH_...
## $ MG_FAR_piglet_rem_ageNUM_NO     <fctr> 0,5, 1, 0,5, 0,5, 1, no, 0,5,...
## $ MG_FAR_piglet_rem_amountCAT     <fctr> 1, 1, 1, 3, 4, 0, 1, 3, 2, 4,...
## $ MG_FAR_pigletremaount_NUM_NO    <fctr> 9, 5, 4, 25, 50, 0, 10, 43, 1...
## $ MG_FAR_piglet_addfeedage        <fctr> 7-14, 7-14, <7, <7, <7, 7-14,...
## $ MG_FAR_bed_yn                   <int> 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...
## $ MG_FAR_bed12345_NO              <int> 0, 14, 12, 25, 0, 1, 1, 12, 1,...
## $ MG_FAR_bedamount                <int> 4, 3, 3, 3, 4, 2, 3, 2, 3, 4, ...
## $ MG_FAR_root_yn                  <int> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_FAR_toy                      <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ MG_FAR_toynum_NO                <int> 0, NA, NA, 0, 4, NA, NA, NA, 0...
## $ MG_FAR_rootamount               <int> 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
## $ MG_FAR_dirt_NUM_NO              <int> 10, 30, 0, 20, 20, 0, 0, NA, 3...
## $ MG_FAR_dirtmed                  <int> 1, 2, 1, 2, 2, 1, 1, NA, 2, NA...
## $ MG_FAR_diranim_NUM_NO           <int> 10, 20, 0, 10, 15, 20, 30, 10,...
## $ MG_FAR_diranimmed               <int> 1, 2, 1, 1, 2, 2, 2, 1, 2, NA,...
## $ MG_FAR_toytoinen_MIKA_NO        <fctr> 0, 2, 2, 2, 2, , 2, 2, 1, 2, ...
## $ OUT_SOW_mort_proNUM             <int> 5, 5, 8, 27, 10, 0, 17, 13, 0,...
## $ OUT_SOW_mort_dic                <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...
## $ OUT_SOW_totremproNUM            <int> 34, 38, 53, 57, 65, 64, 47, 44...
## $ OUT_SOW_totrem_dic              <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, ...
## $ OUT_SOW_cullproNUM              <int> 29, 33, 45, 30, 55, 64, 30, 31...
## $ OUT_SOW_cull_dic                <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "MG_FAR_ox_0_13_46_7" "MG_FAR_dirtmed"      "MG_FAR_diranimmed"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="red") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
MG_FAR_ToFarunitNUM_NO (mean (sd)) 3.04 (1.49) 3.10 (1.45) 0.901
MG_FAR_NestmatdaysNUM_NO (mean (sd)) 4.78 (2.35) 3.95 (2.04) 0.226
MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) 3.70 (1.84) 3.15 (1.87) 0.342
MG_FAR_pigletremaount_NUM_NO (mean (sd)) 11.26 (7.15) 13.05 (6.34) 0.393
MG_FAR_toynum_NO (mean (sd)) 4.31 (1.89) 3.87 (1.73) 0.524
MG_FAR_dirt_NUM_NO (mean (sd)) 2.19 (1.25) 2.59 (1.66) 0.405
MG_FAR_diranim_NUM_NO (mean (sd)) 2.80 (1.67) 3.06 (2.10) 0.679
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
MG_FAR_ind_0no_1rout_2sometimes (%) 0.287
0 10 (43.5) 7 ( 35.0)
1 0 ( 0.0) 2 ( 10.0)
2 13 (56.5) 11 ( 55.0)
MG_FAR_nestmatamount (%) 0.984
0 3 (13.0) 2 ( 10.0)
1 2 ( 8.7) 2 ( 10.0)
2 13 (56.5) 11 ( 55.0)
3 5 (21.7) 5 ( 25.0)
MG_FAR_nestmat_NO (%) 0.363
_CUT 0 ( 0.0) 2 ( 10.0)
_NWS 3 (13.0) 2 ( 10.0)
0 3 (13.0) 2 ( 10.0)
heiina 0 ( 0.0) 1 ( 5.0)
heina 0 ( 0.0) 1 ( 5.0)
heina puruturve 0 ( 0.0) 1 ( 5.0)
heina turve 0 ( 0.0) 1 ( 5.0)
no 0 ( 0.0) 1 ( 5.0)
STR 11 (47.8) 6 ( 30.0)
STR_CUT 5 (21.7) 2 ( 10.0)
STR_CUT_NWS 1 ( 4.3) 0 ( 0.0)
STR_NWS 0 ( 0.0) 1 ( 5.0)
MG_FAR_ox_0_13_46_7 (%) 0.254
0 1 ( 4.3) 0 ( 0.0)
1 10 (43.5) 8 ( 42.1)
2 5 (21.7) 1 ( 5.3)
3 7 (30.4) 10 ( 52.6)
MG_FAR_obstex_preox (%) 0.493
0 15 (65.2) 11 ( 55.0)
1 8 (34.8) 8 ( 40.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_FAR_far_assist_CAT (%) 0.301
<6 4 (17.4) 7 ( 35.0)
10 1 ( 4.3) 0 ( 0.0)
15 0 ( 0.0) 1 ( 5.0)
20-50 4 (17.4) 4 ( 20.0)
50 3 (13.0) 1 ( 5.0)
6-20 10 (43.5) 4 ( 20.0)
noinfo 1 ( 4.3) 3 ( 15.0)
MG_FAR_farassist_MAY_NO (%) 0.350
0 ( 0.0) 2 ( 10.0)
_GLO_LUBR 8 (34.8) 7 ( 35.0)
_HANDWASH_GLO_LUBR 1 ( 4.3) 0 ( 0.0)
GLO_LUBR 0 ( 0.0) 1 ( 5.0)
WASH_GLO_LUBR 8 (34.8) 5 ( 25.0)
WASH_HANDWASH_GLO_LUBR 4 (17.4) 5 ( 25.0)
WASH_HANDWASH_LUBR 2 ( 8.7) 0 ( 0.0)
MG_FAR_piglet_rem_amountCAT (%) 0.001
0 1 ( 4.3) 0 ( 0.0)
1 10 (43.5) 5 ( 25.0)
2 10 (43.5) 1 ( 5.0)
3 0 ( 0.0) 7 ( 35.0)
4 2 ( 8.7) 5 ( 25.0)
noinfo 0 ( 0.0) 2 ( 10.0)
MG_FAR_piglet_addfeedage (%) 0.270
<3 0 ( 0.0) 1 ( 5.0)
<7 14 (60.9) 8 ( 40.0)
>20 1 ( 4.3) 0 ( 0.0)
7-14 8 (34.8) 11 ( 55.0)
MG_FAR_bed_yn = 1 (%) 17 (73.9) 16 ( 80.0) 0.913
MG_FAR_bed12345_NO (%) 0.374
0 6 (26.1) 4 ( 20.0)
1 2 ( 8.7) 1 ( 5.0)
2 4 (17.4) 7 ( 35.0)
4 1 ( 4.3) 0 ( 0.0)
5 1 ( 4.3) 0 ( 0.0)
12 6 (26.1) 4 ( 20.0)
14 2 ( 8.7) 0 ( 0.0)
15 1 ( 4.3) 0 ( 0.0)
24 0 ( 0.0) 1 ( 5.0)
25 0 ( 0.0) 2 ( 10.0)
245 0 ( 0.0) 1 ( 5.0)
MG_FAR_bedamount (%) 0.150
0 0 ( 0.0) 1 ( 5.0)
1 4 (17.4) 1 ( 5.0)
2 3 (13.0) 8 ( 40.0)
3 9 (39.1) 7 ( 35.0)
4 7 (30.4) 3 ( 15.0)
MG_FAR_root_yn = 1 (%) 18 (78.3) 17 ( 85.0) 0.862
MG_FAR_toy = 1 (%) 11 (47.8) 12 ( 60.0) 0.623
MG_FAR_rootamount (%) 0.263
0 2 ( 8.7) 3 ( 15.0)
1 5 (21.7) 1 ( 5.0)
2 16 (69.6) 16 ( 80.0)
MG_FAR_dirtmed = 2 (%) 7 (33.3) 8 ( 47.1) 0.598
MG_FAR_diranimmed = 2 (%) 9 (45.0) 8 ( 44.4) 1.000
MG_FAR_toytoinen_MIKA_NO (%) 0.525
2 ( 8.7) 0 ( 0.0)
0 1 ( 4.3) 1 ( 5.0)
1 6 (26.1) 3 ( 15.0)
2 11 (47.8) 14 ( 70.0)
3 2 ( 8.7) 2 ( 10.0)
noinfo 1 ( 4.3) 0 ( 0.0)
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
MG_FAR_ToFarunitNUM_NO (mean (sd)) 2.77 (1.38) 3.38 (1.50) 0.173
MG_FAR_NestmatdaysNUM_NO (mean (sd)) 4.55 (2.09) 4.24 (2.41) 0.656
MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) 3.64 (1.62) 3.24 (2.10) 0.488
MG_FAR_pigletremaount_NUM_NO (mean (sd)) 11.64 (6.24) 12.57 (7.40) 0.656
MG_FAR_toynum_NO (mean (sd)) 3.85 (1.95) 4.27 (1.67) 0.544
MG_FAR_dirt_NUM_NO (mean (sd)) 2.16 (1.26) 2.58 (1.61) 0.375
MG_FAR_diranim_NUM_NO (mean (sd)) 2.63 (1.54) 3.21 (2.15) 0.346
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
MG_FAR_ind_0no_1rout_2sometimes (%) 0.164
0 11 (50.0) 6 ( 28.6)
1 0 ( 0.0) 2 ( 9.5)
2 11 (50.0) 13 ( 61.9)
MG_FAR_nestmatamount (%) 0.038
0 2 ( 9.1) 3 ( 14.3)
1 4 (18.2) 0 ( 0.0)
2 14 (63.6) 10 ( 47.6)
3 2 ( 9.1) 8 ( 38.1)
MG_FAR_nestmat_NO (%) 0.210
_CUT 2 ( 9.1) 0 ( 0.0)
_NWS 2 ( 9.1) 3 ( 14.3)
0 2 ( 9.1) 3 ( 14.3)
heiina 1 ( 4.5) 0 ( 0.0)
heina 0 ( 0.0) 1 ( 4.8)
heina puruturve 0 ( 0.0) 1 ( 4.8)
heina turve 1 ( 4.5) 0 ( 0.0)
no 0 ( 0.0) 1 ( 4.8)
STR 11 (50.0) 6 ( 28.6)
STR_CUT 1 ( 4.5) 6 ( 28.6)
STR_CUT_NWS 1 ( 4.5) 0 ( 0.0)
STR_NWS 1 ( 4.5) 0 ( 0.0)
MG_FAR_ox_0_13_46_7 (%) 0.652
0 1 ( 4.5) 0 ( 0.0)
1 9 (40.9) 9 ( 45.0)
2 4 (18.2) 2 ( 10.0)
3 8 (36.4) 9 ( 45.0)
MG_FAR_obstex_preox (%) 0.541
0 13 (59.1) 13 ( 61.9)
1 9 (40.9) 7 ( 33.3)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_FAR_far_assist_CAT (%) 0.289
<6 6 (27.3) 5 ( 23.8)
10 0 ( 0.0) 1 ( 4.8)
15 1 ( 4.5) 0 ( 0.0)
20-50 4 (18.2) 4 ( 19.0)
50 4 (18.2) 0 ( 0.0)
6-20 6 (27.3) 8 ( 38.1)
noinfo 1 ( 4.5) 3 ( 14.3)
MG_FAR_farassist_MAY_NO (%) 0.463
0 ( 0.0) 2 ( 9.5)
_GLO_LUBR 9 (40.9) 6 ( 28.6)
_HANDWASH_GLO_LUBR 0 ( 0.0) 1 ( 4.8)
GLO_LUBR 0 ( 0.0) 1 ( 4.8)
WASH_GLO_LUBR 6 (27.3) 7 ( 33.3)
WASH_HANDWASH_GLO_LUBR 6 (27.3) 3 ( 14.3)
WASH_HANDWASH_LUBR 1 ( 4.5) 1 ( 4.8)
MG_FAR_piglet_rem_amountCAT (%) 0.088
0 0 ( 0.0) 1 ( 4.8)
1 10 (45.5) 5 ( 23.8)
2 6 (27.3) 5 ( 23.8)
3 5 (22.7) 2 ( 9.5)
4 1 ( 4.5) 6 ( 28.6)
noinfo 0 ( 0.0) 2 ( 9.5)
MG_FAR_piglet_addfeedage (%) 0.530
<3 0 ( 0.0) 1 ( 4.8)
<7 12 (54.5) 10 ( 47.6)
>20 1 ( 4.5) 0 ( 0.0)
7-14 9 (40.9) 10 ( 47.6)
MG_FAR_bed_yn = 1 (%) 18 (81.8) 15 ( 71.4) 0.656
MG_FAR_bed12345_NO (%) 0.798
0 4 (18.2) 6 ( 28.6)
1 2 ( 9.1) 1 ( 4.8)
2 5 (22.7) 6 ( 28.6)
4 0 ( 0.0) 1 ( 4.8)
5 1 ( 4.5) 0 ( 0.0)
12 6 (27.3) 4 ( 19.0)
14 1 ( 4.5) 1 ( 4.8)
15 1 ( 4.5) 0 ( 0.0)
24 0 ( 0.0) 1 ( 4.8)
25 1 ( 4.5) 1 ( 4.8)
245 1 ( 4.5) 0 ( 0.0)
MG_FAR_bedamount (%) 0.538
0 0 ( 0.0) 1 ( 4.8)
1 4 (18.2) 1 ( 4.8)
2 6 (27.3) 5 ( 23.8)
3 7 (31.8) 9 ( 42.9)
4 5 (22.7) 5 ( 23.8)
MG_FAR_root_yn = 1 (%) 19 (86.4) 16 ( 76.2) 0.642
MG_FAR_toy = 1 (%) 10 (45.5) 13 ( 61.9) 0.438
MG_FAR_rootamount (%) 0.277
0 1 ( 4.5) 4 ( 19.0)
1 4 (18.2) 2 ( 9.5)
2 17 (77.3) 15 ( 71.4)
MG_FAR_dirtmed = 2 (%) 7 (36.8) 8 ( 42.1) 1.000
MG_FAR_diranimmed = 2 (%) 7 (36.8) 10 ( 52.6) 0.514
MG_FAR_toytoinen_MIKA_NO (%) 0.801
1 ( 4.5) 1 ( 4.8)
0 1 ( 4.5) 1 ( 4.8)
1 3 (13.6) 6 ( 28.6)
2 14 (63.6) 11 ( 52.4)
3 2 ( 9.1) 2 ( 9.5)
noinfo 1 ( 4.5) 0 ( 0.0)
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(18,19),quali.sup=c(16:17), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(18, 19), quali.sup = c(16:17),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.326   0.271   0.212   0.183   0.157   0.147
## % of var.             12.520  10.406   8.173   7.044   6.057   5.661
## Cumulative % of var.  12.520  22.926  31.099  38.142  44.199  49.860
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.138   0.126   0.120   0.103   0.097   0.087
## % of var.              5.316   4.862   4.624   3.977   3.726   3.361
## Cumulative % of var.  55.176  60.038  64.662  68.639  72.365  75.726
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.081   0.073   0.064   0.056   0.055   0.048
## % of var.              3.106   2.798   2.467   2.147   2.117   1.848
## Cumulative % of var.  78.832  81.630  84.096  86.243  88.360  90.208
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.042   0.036   0.032   0.030   0.023   0.020
## % of var.              1.612   1.385   1.234   1.149   0.867   0.760
## Cumulative % of var.  91.820  93.205  94.439  95.588  96.456  97.216
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.017   0.015   0.011   0.010   0.007   0.004
## % of var.              0.638   0.577   0.429   0.367   0.273   0.168
## Cumulative % of var.  97.854  98.431  98.860  99.228  99.501  99.668
##                       Dim.31  Dim.32  Dim.33  Dim.34  Dim.35  Dim.36
## Variance               0.004   0.003   0.002   0.001   0.000   0.000
## % of var.              0.153   0.098   0.059   0.021   0.000   0.000
## Cumulative % of var.  99.822  99.920  99.979 100.000 100.000 100.000
##                       Dim.37  Dim.38  Dim.39
## Variance               0.000   0.000   0.000
## % of var.              0.000   0.000   0.000
## Cumulative % of var. 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                                      Dim.1    ctr   cos2    Dim.2    ctr
## 1                                 |  0.404  1.165  0.057 | -0.969  8.069
## 2                                 | -0.273  0.533  0.056 |  0.221  0.420
## 3                                 | -0.290  0.601  0.080 |  0.099  0.084
## 4                                 | -0.131  0.123  0.011 |  0.065  0.036
## 5                                 |  0.595  2.527  0.153 | -0.227  0.444
## 6                                 | -0.159  0.181  0.004 |  0.098  0.083
## 7                                 |  0.165  0.195  0.007 |  0.053  0.024
## 8                                 | -0.253  0.458  0.022 |  0.320  0.881
## 9                                 | -0.296  0.627  0.055 |  0.287  0.707
## 10                                | -0.016  0.002  0.000 |  0.001  0.000
##                                     cos2    Dim.3    ctr   cos2  
## 1                                  0.330 |  0.089  0.087  0.003 |
## 2                                  0.037 | -0.020  0.004  0.000 |
## 3                                  0.009 | -0.265  0.767  0.066 |
## 4                                  0.003 | -0.453  2.242  0.134 |
## 5                                  0.022 |  0.099  0.108  0.004 |
## 6                                  0.001 | -0.631  4.356  0.061 |
## 7                                  0.001 | -0.268  0.788  0.018 |
## 8                                  0.035 | -0.556  3.382  0.105 |
## 9                                  0.052 | -0.107  0.125  0.007 |
## 10                                 0.000 | -0.401  1.764  0.053 |
## 
## Categories (the 10 first)
##                                      Dim.1    ctr   cos2 v.test    Dim.2
## MG_FAR_ind_0no_1rout_2sometimes_0 | -0.575  2.675  0.216 -3.012 |  0.311
## MG_FAR_ind_0no_1rout_2sometimes_1 |  1.938  3.577  0.183  2.774 | -1.396
## MG_FAR_ind_0no_1rout_2sometimes_2 |  0.246  0.690  0.076  1.789 | -0.104
## MG_FAR_nestmatamount_0            |  1.236  3.638  0.201  2.905 | -1.159
## MG_FAR_nestmatamount_1            | -0.791  1.192  0.064 -1.642 |  0.566
## MG_FAR_nestmatamount_2            | -0.431  2.121  0.234 -3.138 |  0.213
## MG_FAR_nestmatamount_3            |  0.732  2.555  0.163  2.613 | -0.159
## MG_FAR_ox_0_13_46_7_0             | -1.591  1.206  0.060 -1.591 |  1.484
## MG_FAR_ox_0_13_46_7_1             | -0.439  1.649  0.138 -2.412 |  0.097
## MG_FAR_ox_0_13_46_7_2             | -0.533  0.813  0.046 -1.392 |  0.383
##                                      ctr   cos2 v.test    Dim.3    ctr
## MG_FAR_ind_0no_1rout_2sometimes_0  0.942  0.063  1.629 |  0.270  0.906
## MG_FAR_ind_0no_1rout_2sometimes_1  2.233  0.095 -1.998 |  1.818  4.825
## MG_FAR_ind_0no_1rout_2sometimes_2  0.148  0.014 -0.757 | -0.343  2.060
## MG_FAR_nestmatamount_0             3.852  0.177 -2.726 |  0.904  2.979
## MG_FAR_nestmatamount_1             0.734  0.033  1.174 |  0.205  0.122
## MG_FAR_nestmatamount_2             0.626  0.058  1.554 | -0.177  0.547
## MG_FAR_nestmatamount_3             0.144  0.008 -0.566 | -0.109  0.087
## MG_FAR_ox_0_13_46_7_0              1.262  0.052  1.484 |  4.374 13.957
## MG_FAR_ox_0_13_46_7_1              0.097  0.007  0.533 | -0.066  0.057
## MG_FAR_ox_0_13_46_7_2              0.504  0.024  0.999 | -0.284  0.352
##                                     cos2 v.test  
## MG_FAR_ind_0no_1rout_2sometimes_0  0.048  1.416 |
## MG_FAR_ind_0no_1rout_2sometimes_1  0.161  2.603 |
## MG_FAR_ind_0no_1rout_2sometimes_2  0.149 -2.498 |
## MG_FAR_nestmatamount_0             0.107  2.124 |
## MG_FAR_nestmatamount_1             0.004  0.425 |
## MG_FAR_nestmatamount_2             0.039 -1.288 |
## MG_FAR_nestmatamount_3             0.004 -0.391 |
## MG_FAR_ox_0_13_46_7_0              0.455  4.374 |
## MG_FAR_ox_0_13_46_7_1              0.003 -0.363 |
## MG_FAR_ox_0_13_46_7_2              0.013 -0.740 |
## 
## Categorical variables (eta2)
##                                     Dim.1 Dim.2 Dim.3  
## MG_FAR_ind_0no_1rout_2sometimes   | 0.339 0.135 0.248 |
## MG_FAR_nestmatamount              | 0.464 0.217 0.119 |
## MG_FAR_ox_0_13_46_7               | 0.710 0.618 0.461 |
## MG_FAR_obstex_preox               | 0.452 0.428 0.032 |
## MG_FAR_far_assist_CAT             | 0.583 0.142 0.344 |
## MG_FAR_piglet_rem_amountCAT       | 0.647 0.242 0.177 |
## MG_FAR_piglet_addfeedage          | 0.502 0.487 0.472 |
## MG_FAR_bed_yn                     | 0.067 0.365 0.021 |
## MG_FAR_bedamount                  | 0.314 0.370 0.440 |
## MG_FAR_root_yn                    | 0.243 0.381 0.089 |
## 
## Supplementary categories
##                                      Dim.1   cos2 v.test    Dim.2   cos2
## OUT_SOW_totrem_dic_0              | -0.290  0.080 -1.836 | -0.052  0.003
## OUT_SOW_totrem_dic_1              |  0.277  0.080  1.836 |  0.050  0.003
## OUT_SOW_cull_dic_0                | -0.309  0.100 -2.050 |  0.055  0.003
## OUT_SOW_cull_dic_1                |  0.324  0.100  2.050 | -0.057  0.003
##                                   v.test    Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0              -0.329 |  0.139  0.018  0.880 |
## OUT_SOW_totrem_dic_1               0.329 | -0.133  0.018 -0.880 |
## OUT_SOW_cull_dic_0                 0.363 | -0.010  0.000 -0.067 |
## OUT_SOW_cull_dic_1                -0.363 |  0.011  0.000  0.067 |
## 
## Supplementary categorical variables (eta2)
##                                     Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic                | 0.080 0.003 0.018 |
## OUT_SOW_cull_dic                  | 0.100 0.003 0.000 |
## 
## Supplementary continuous variables
##                                      Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM               |  0.234 | -0.037 | -0.135 |
## OUT_SOW_cullproNUM                |  0.332 |  0.073 | -0.129 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("13", "39", "43", "1", "24", "21", "18", "42", "31", "16")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by16* and *16*. :
## 
## - high frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_1*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_FAR_bedamount=MG_FAR_bedamount_2*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_1* and *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_0*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0*, *MG_FAR_rootamount=MG_FAR_rootamount_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_3*, *MG_FAR_bedamount=MG_FAR_bedamount_4*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_noinfo* and *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by1* and *1*. :
## 
## - high frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_0*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_FAR_rootamount=MG_FAR_rootamount_0*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0*, *MG_FAR_bedamount=MG_FAR_bedamount_4*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_3*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_2* and *MG_FAR_diranimmed=MG_FAR_diranimmed_1* (factors are sorted from the most common).
## - low frequency for the factors *MG_FAR_root_yn=MG_FAR_root_yn_1*, *MG_FAR_nestmatamount=MG_FAR_nestmatamount_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_bedamount=MG_FAR_bedamount_2* and *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_1* (factors are sorted from the rarest).
## 
## The 1st cluster is made of individuals such as *13*. This group is characterized by :
## 
## - high frequency for the factors *MG_FAR_piglet_addfeedage=<3*, *MG_FAR_obstex_preox=MG_FAR_obstex_preox_noinfo*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_Not Assigned* and *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_noinfo* (factors are sorted from the most common).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Management

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="manag.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 85
## $ MG_feedtimes_preg                     <int> 2, 2, 1, 2, NA, 2, 2, 2,...
## $ MG_feedtimes_far                      <int> 3, 3, 3, 4, NA, 3, 4, 3,...
## $ MG_feed_liq_solid                     <fctr> liq, liq, liq, liq, , l...
## $ MG_owngilts                           <int> 90, 100, 95, 0, 0, 100, ...
## $ MG_owngilts.1                         <int> 1, 1, 1, 0, 0, 1, 1, 0, ...
## $ MG_BR_giltpurchage_NUM_NO             <int> 4, 0, 0, 7, 3, 0, 0, 5, ...
## $ MG_BR_giltchangebeforeins_NO          <int> 0, 0, 1, 0, 1, 0, 1, 0, ...
## $ MG_BR_giltflush_NO                    <fctr> 0, 1, 0, 0, 1, 0, 0, 1,...
## $ MG_BR_giltboarstart_NO                <fctr> 7, 6, 7,5, 7,5, 7, 7,5,...
## $ MG_BR_giltinsage_NO                   <fctr> 8, 7, 8, 7,5, 8, 9,5, 8...
## $ MG_BR_heatgroup_NO                    <fctr> 0, 1, 1, 1, 1, 1, 1, 1,...
## $ MG_BR_heatdetec_startNUM_NO           <fctr> 0, 0, 5, 0, 1, 0, 3, 3,...
## $ MG_BR_heatmarkback_NO                 <fctr> 1, 0, 1, 1, 1, 1, 1, 0,...
## $ MG_BR_artinspro_050_5099_100          <int> 1, 1, 2, 2, 2, 1, 1, 2, ...
## $ MG_BR_farmsemenNUM_NO                 <int> 0, 0, 95, 0, 50, 0, 0, 0...
## $ MG_BR_insonceNUM_NO                   <fctr> 0, 8, 0, 10, 0, 10, 80,...
## $ MG_BR_once_012                        <int> 0, 1, 0, 1, 0, 1, 2, 1, ...
## $ MG_BR_instriple_NO                    <fctr> 1, 2, 10, 10, 15, 0, 0,...
## $ MG_BR_triple_012                      <fctr> 1, 1, 1, 1, 2, 0, 0, 1,...
## $ MG_aveins                             <fctr> 2, 2, 2, 2, 2, 2, 2, 2,...
## $ MG_BR_nopregus                        <fctr> 1, 1, 2, 1, 2, 0, 0, 1,...
## $ MG_FAR_ToFarunitNUM_NO                <int> 6, 5, 4, 7, 7, 7, 3, 5, ...
## $ MG_FAR_ind_0no_1rout_2sometimes       <int> 2, 0, 2, 2, 2, 2, 2, 0, ...
## $ MG_FAR_NestmatdaysNUM_NO              <int> 6, 1, 4, 7, 2, 0, 3, 2, ...
## $ MG_FAR_nestmatamount                  <int> 3, 2, 2, 2, 3, 2, 3, 1, ...
## $ MG_FAR_nestmat_NO                     <fctr> STR, STR, STR_CUT, heii...
## $ MG_FAR_ox_0_13_46_7                   <int> 3, 1, 1, 1, 3, 3, 3, 2, ...
## $ MG_FAR_obstex_preox                   <fctr> 1, 0, 0, 1, 0, 0, 0, 1,...
## $ MG_FAR_far_assist_CAT                 <fctr> 50, 20-50, <6, 20-50, n...
## $ MG_FAR_farassist_MAY_NO               <fctr> WASH_GLO_LUBR, WASH_HAN...
## $ MG_FAR_piglet_rem_ageNUM_NO           <fctr> 0,5, 1, 0,5, 0,5, 1, no...
## $ MG_FAR_piglet_rem_amountCAT           <fctr> 1, 1, 1, 3, 4, 0, 1, 3,...
## $ MG_FAR_pigletremaount_NUM_NO          <fctr> 9, 5, 4, 25, 50, 0, 10,...
## $ MG_FAR_piglet_addfeedage              <fctr> 7-14, 7-14, <7, <7, <7,...
## $ MG_ind_feed                           <int> 1, 1, 1, 1, 1, 1, 1, 0, ...
## $ MG_BR_bedtype_NO                      <int> 0, 1, 12, 0, 0, 14, 1, 1...
## $ MG_BR_bedny                           <int> 0, 1, 1, 0, 0, 1, 1, 1, ...
## $ MG_BR_amount                          <int> 4, 2, 1, 0, 0, 1, 3, 2, ...
## $ MG_BR_rootny                          <int> 0, 1, 1, 0, 0, 1, 0, 1, ...
## $ MG_BR_toyny                           <fctr> 0, 0, 0, 0, 1, 0, 0, 1,...
## $ MG_sickpen_yn                         <int> 0, 1, 1, 1, 1, 0, 1, 1, ...
## $ MG_BR_dirt_NUM_NO                     <int> 30, 0, 0, 20, 40, 0, 20,...
## $ MG_BR_animdirtmed                     <int> 2, 1, 1, 2, 2, 1, 2, 2, ...
## $ MG_BR_feedtype                        <int> 4, 1, 25, 4, 4, 4, 4, 4,...
## $ MG_BR_feedclean                       <fctr> 0, 0, 0, 0, 1, 0, 0, 1,...
## $ MG_BR_calm                            <int> 2, 1, 1, 1, 1, 1, 2, 1, ...
## $ MG_BR_dirtanim_NUM_NO                 <int> 20, 0, 10, 10, NA, 20, 3...
## $ MG_BR_dirtanimmed                     <int> 2, 1, 1, 1, NA, 2, 2, 2,...
## $ MG_BR_ster                            <int> 1, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_earlyHAR_kaNUM                  <fctr> 0,9, 0,15, 0, 0,2, 1, 0...
## $ MG_PR_type                            <int> 2, 1, 12, 13, 2, 1, 2, 1...
## $ MG_PR_rootyn                          <int> 1, 1, 1, 0, 1, 1, 1, 1, ...
## $ MG_PR_toyyn                           <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_toy                             <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_rootamount                         <int> 2, 2, 1, 0, 1, 1, 2, 1, ...
## $ MG_PR_kuivaliete                      <int> 2, 1, 1, 2, 2, 1, 2, 1, ...
## $ MG_PR_ruok_0nonlock_1lock             <int> 0, 0, 0, 1, 0, 1, 1, 0, ...
## $ MG_PR_feedtype                        <int> 3, 1, 25, 4, 5, 4, 4, 5,...
## $ MG_PR_calm                            <int> 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_dirt_NUM_NO                     <int> 20, 0, 10, 20, 20, 20, 2...
## $ MG_PR_animdirtmed                     <int> 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_PR_ster                            <int> 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ MG_PR_late_HAR_kaNUM_NO               <fctr> 0,9, 0,15, 0, 0,1, , , ...
## $ MG_FAR_bed_yn                         <int> 0, 1, 1, 1, 0, 1, 1, 1, ...
## $ MG_FAR_bed12345_NO                    <int> 0, 14, 12, 25, 0, 1, 1, ...
## $ MG_FAR_bedamount                      <int> 4, 3, 3, 3, 4, 2, 3, 2, ...
## $ MG_FAR_root_yn                        <int> 0, 1, 1, 1, 1, 1, 1, 1, ...
## $ MG_FAR_toy                            <int> 0, 0, 0, 0, 1, 0, 0, 0, ...
## $ MG_FAR_toynum_NO                      <int> 0, NA, NA, 0, 4, NA, NA,...
## $ MG_FAR_rootamount                     <int> 0, 2, 2, 2, 2, 2, 2, 2, ...
## $ MG_FAR_dirt_NUM_NO                    <int> 10, 30, 0, 20, 20, 0, 0,...
## $ MG_FAR_dirtmed                        <int> 1, 2, 1, 2, 2, 1, 1, NA,...
## $ MG_FAR_diranim_NUM_NO                 <int> 10, 20, 0, 10, 15, 20, 3...
## $ MG_FAR_diranimmed                     <int> 1, 2, 1, 1, 2, 2, 2, 1, ...
## $ MG_BR_sowsperboar_NUM_NO              <fctr> 150, 37, 75, 92, 525, 3...
## $ MG_FAR_toytoinen_MIKA_NO              <fctr> 0, 2, 2, 2, 2, , 2, 2, ...
## $ MG_SOWSperworkeredit_NUM57_113_147_NO <int> 158, 20, 113, 126, 340, ...
## $ MG_SOWSperworkeredit_57_113_147_      <int> 4, 1, 2, 3, 4, 1, 2, 3, ...
## $ MG_SOWSperworker_NUM_NO               <int> 150, NA, 100, 138, 350, ...
## $ OUT_SOW_mort_proNUM                   <int> 5, 5, 8, 27, 10, 0, 17, ...
## $ OUT_SOW_mort_dic                      <int> 0, 0, 0, 1, 1, 0, 1, 1, ...
## $ OUT_SOW_totremproNUM                  <int> 34, 38, 53, 57, 65, 64, ...
## $ OUT_SOW_totrem_dic                    <int> 0, 0, 1, 1, 1, 1, 1, 0, ...
## $ OUT_SOW_cullproNUM                    <int> 29, 33, 45, 30, 55, 64, ...
## $ OUT_SOW_cull_dic                      <int> 0, 0, 1, 0, 1, 1, 0, 0, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "MG_feedtimes_preg"   "MG_feedtimes_far"    "MG_BR_once_012"     
## [4] "MG_FAR_ox_0_13_46_7" "MG_BR_animdirtmed"   "MG_BR_dirtanimmed"  
## [7] "MG_PR_animdirtmed"   "MG_FAR_dirtmed"      "MG_FAR_diranimmed"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="lightblue") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
MG_BR_giltpurchage_NUM_NO (mean (sd)) 3.00 (2.63) 2.40 (2.26) 0.430
MG_BR_heatdetec_startNUM_NO (mean (sd)) 3.35 (2.44) 3.55 (2.37) 0.785
MG_BR_farmsemenNUM_NO (mean (sd)) 1.96 (2.06) 2.00 (1.97) 0.944
MG_BR_insonceNUM_NO (mean (sd)) 4.04 (3.75) 6.15 (3.99) 0.082
MG_FAR_ToFarunitNUM_NO (mean (sd)) 3.04 (1.49) 3.10 (1.45) 0.901
MG_FAR_NestmatdaysNUM_NO (mean (sd)) 4.78 (2.35) 3.95 (2.04) 0.226
MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) 3.70 (1.84) 3.15 (1.87) 0.342
MG_FAR_pigletremaount_NUM_NO (mean (sd)) 11.26 (7.15) 13.05 (6.34) 0.393
MG_BR_dirt_NUM_NO (mean (sd)) 2.27 (1.52) 2.59 (1.54) 0.527
MG_BR_dirtanim_NUM_NO (mean (sd)) 2.52 (1.54) 2.79 (1.23) 0.552
MG_PR_dirt_NUM_NO (mean (sd)) 3.00 (1.95) 4.26 (2.38) 0.066
MG_PR_late_HAR_kaNUM_NO (mean (sd)) 4.87 (4.70) 5.95 (5.57) 0.494
MG_FAR_toynum_NO (mean (sd)) 4.31 (1.89) 3.87 (1.73) 0.524
MG_FAR_dirt_NUM_NO (mean (sd)) 2.19 (1.25) 2.59 (1.66) 0.405
MG_FAR_diranim_NUM_NO (mean (sd)) 2.80 (1.67) 3.06 (2.10) 0.679
MG_BR_sowsperboar_NUM_NO (mean (sd)) 19.65 (10.08) 17.55 (11.15) 0.520
MG_SOWSperworker_NUM_NO (mean (sd)) 16.27 (9.73) 21.11 (10.05) 0.126
MG_PR_earlyHAR_kaNUM (mean (sd)) 7.91 (4.73) 10.15 (5.41) 0.156
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
MG_feedtimes_preg (%) 0.617
1 1 ( 4.5) 1 ( 5.3)
2 17 ( 77.3) 17 ( 89.5)
3 3 ( 13.6) 1 ( 5.3)
4 1 ( 4.5) 0 ( 0.0)
MG_feedtimes_far (%) 0.099
2 4 ( 18.2) 0 ( 0.0)
3 13 ( 59.1) 16 ( 84.2)
4 5 ( 22.7) 3 ( 15.8)
MG_feed_liq_solid (%) 0.664
2 ( 8.7) 1 ( 5.0)
liq 12 ( 52.2) 12 ( 60.0)
liqsol 1 ( 4.3) 1 ( 5.0)
sol 6 ( 26.1) 5 ( 25.0)
solid 2 ( 8.7) 0 ( 0.0)
solliq 0 ( 0.0) 1 ( 5.0)
MG_owngilts (%) 0.895
0 8 ( 34.8) 7 ( 35.0)
50 0 ( 0.0) 1 ( 5.0)
70 1 ( 4.3) 0 ( 0.0)
80 1 ( 4.3) 1 ( 5.0)
90 2 ( 8.7) 1 ( 5.0)
95 1 ( 4.3) 1 ( 5.0)
100 10 ( 43.5) 9 ( 45.0)
MG_owngilts.1 = 1 (%) 15 ( 65.2) 13 ( 65.0) 1.000
MG_BR_giltchangebeforeins_NO = 1 (%) 9 ( 39.1) 10 ( 50.0) 0.683
MG_BR_giltflush_NO (%) 0.299
0 13 ( 56.5) 10 ( 50.0)
1 8 ( 34.8) 10 ( 50.0)
noneed 2 ( 8.7) 0 ( 0.0)
MG_BR_giltboarstart_NO (%) 0.158
0 4 ( 17.4) 0 ( 0.0)
3 1 ( 4.3) 0 ( 0.0)
4 0 ( 0.0) 1 ( 5.0)
6 4 ( 17.4) 4 ( 20.0)
6,5 3 ( 13.0) 0 ( 0.0)
7 6 ( 26.1) 10 ( 50.0)
7,5 4 ( 17.4) 4 ( 20.0)
8 1 ( 4.3) 0 ( 0.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_giltinsage_NO (%) 0.139
0 3 ( 13.0) 0 ( 0.0)
2ndheat 0 ( 0.0) 1 ( 5.0)
6 0 ( 0.0) 1 ( 5.0)
7 2 ( 8.7) 0 ( 0.0)
7,5 1 ( 4.3) 1 ( 5.0)
8 14 ( 60.9) 11 ( 55.0)
8,5 0 ( 0.0) 4 ( 20.0)
9 1 ( 4.3) 0 ( 0.0)
9,5 2 ( 8.7) 1 ( 5.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_heatgroup_NO (%) 0.361
0 5 ( 21.7) 4 ( 20.0)
1 17 ( 73.9) 14 ( 70.0)
no 1 ( 4.3) 0 ( 0.0)
noinfo 0 ( 0.0) 2 ( 10.0)
MG_BR_heatmarkback_NO (%) 0.211
0 8 ( 34.8) 3 ( 15.0)
1 15 ( 65.2) 16 ( 80.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_BR_artinspro_050_5099_100 (%) 0.785
0 1 ( 4.3) 1 ( 5.0)
1 8 ( 34.8) 5 ( 25.0)
2 14 ( 60.9) 14 ( 70.0)
MG_BR_once_012 (%) 0.059
0 10 ( 45.5) 5 ( 26.3)
1 12 ( 54.5) 10 ( 52.6)
2 0 ( 0.0) 4 ( 21.1)
MG_BR_instriple_NO (%) 0.630
0 7 ( 30.4) 4 ( 20.0)
1 1 ( 4.3) 2 ( 10.0)
10 5 ( 21.7) 6 ( 30.0)
15 0 ( 0.0) 2 ( 10.0)
2 1 ( 4.3) 0 ( 0.0)
3 1 ( 4.3) 1 ( 5.0)
30 1 ( 4.3) 0 ( 0.0)
33 0 ( 0.0) 1 ( 5.0)
5 6 ( 26.1) 3 ( 15.0)
noinfo 1 ( 4.3) 1 ( 5.0)
MG_BR_triple_012 (%) 0.621
0 7 ( 30.4) 4 ( 20.0)
1 14 ( 60.9) 12 ( 60.0)
2 1 ( 4.3) 3 ( 15.0)
noinfo 1 ( 4.3) 1 ( 5.0)
MG_aveins (%) 0.299
1 0 ( 0.0) 1 ( 5.0)
2 23 (100.0) 18 ( 90.0)
2,1 0 ( 0.0) 1 ( 5.0)
MG_BR_nopregus (%) 0.179
0 10 ( 43.5) 3 ( 15.0)
1 10 ( 43.5) 12 ( 60.0)
2 3 ( 13.0) 4 ( 20.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_FAR_ind_0no_1rout_2sometimes (%) 0.287
0 10 ( 43.5) 7 ( 35.0)
1 0 ( 0.0) 2 ( 10.0)
2 13 ( 56.5) 11 ( 55.0)
MG_FAR_nestmatamount (%) 0.984
0 3 ( 13.0) 2 ( 10.0)
1 2 ( 8.7) 2 ( 10.0)
2 13 ( 56.5) 11 ( 55.0)
3 5 ( 21.7) 5 ( 25.0)
MG_FAR_nestmat_NO (%) 0.363
_CUT 0 ( 0.0) 2 ( 10.0)
_NWS 3 ( 13.0) 2 ( 10.0)
0 3 ( 13.0) 2 ( 10.0)
heiina 0 ( 0.0) 1 ( 5.0)
heina 0 ( 0.0) 1 ( 5.0)
heina puruturve 0 ( 0.0) 1 ( 5.0)
heina turve 0 ( 0.0) 1 ( 5.0)
no 0 ( 0.0) 1 ( 5.0)
STR 11 ( 47.8) 6 ( 30.0)
STR_CUT 5 ( 21.7) 2 ( 10.0)
STR_CUT_NWS 1 ( 4.3) 0 ( 0.0)
STR_NWS 0 ( 0.0) 1 ( 5.0)
MG_FAR_ox_0_13_46_7 (%) 0.254
0 1 ( 4.3) 0 ( 0.0)
1 10 ( 43.5) 8 ( 42.1)
2 5 ( 21.7) 1 ( 5.3)
3 7 ( 30.4) 10 ( 52.6)
MG_FAR_obstex_preox (%) 0.493
0 15 ( 65.2) 11 ( 55.0)
1 8 ( 34.8) 8 ( 40.0)
noinfo 0 ( 0.0) 1 ( 5.0)
MG_FAR_far_assist_CAT (%) 0.301
<6 4 ( 17.4) 7 ( 35.0)
10 1 ( 4.3) 0 ( 0.0)
15 0 ( 0.0) 1 ( 5.0)
20-50 4 ( 17.4) 4 ( 20.0)
50 3 ( 13.0) 1 ( 5.0)
6-20 10 ( 43.5) 4 ( 20.0)
noinfo 1 ( 4.3) 3 ( 15.0)
MG_FAR_farassist_MAY_NO (%) 0.350
0 ( 0.0) 2 ( 10.0)
_GLO_LUBR 8 ( 34.8) 7 ( 35.0)
_HANDWASH_GLO_LUBR 1 ( 4.3) 0 ( 0.0)
GLO_LUBR 0 ( 0.0) 1 ( 5.0)
WASH_GLO_LUBR 8 ( 34.8) 5 ( 25.0)
WASH_HANDWASH_GLO_LUBR 4 ( 17.4) 5 ( 25.0)
WASH_HANDWASH_LUBR 2 ( 8.7) 0 ( 0.0)
MG_FAR_piglet_rem_amountCAT (%) 0.001
0 1 ( 4.3) 0 ( 0.0)
1 10 ( 43.5) 5 ( 25.0)
2 10 ( 43.5) 1 ( 5.0)
3 0 ( 0.0) 7 ( 35.0)
4 2 ( 8.7) 5 ( 25.0)
noinfo 0 ( 0.0) 2 ( 10.0)
MG_FAR_piglet_addfeedage (%) 0.270
<3 0 ( 0.0) 1 ( 5.0)
<7 14 ( 60.9) 8 ( 40.0)
>20 1 ( 4.3) 0 ( 0.0)
7-14 8 ( 34.8) 11 ( 55.0)
MG_ind_feed = 1 (%) 17 ( 73.9) 14 ( 70.0) 1.000
MG_BR_bedtype_NO (%) 0.229
0 7 ( 30.4) 11 ( 55.0)
1 7 ( 30.4) 4 ( 20.0)
2 1 ( 4.3) 4 ( 20.0)
5 1 ( 4.3) 0 ( 0.0)
12 3 ( 13.0) 1 ( 5.0)
14 2 ( 8.7) 0 ( 0.0)
25 1 ( 4.3) 0 ( 0.0)
125 1 ( 4.3) 0 ( 0.0)
MG_BR_bedny = 1 (%) 16 ( 69.6) 9 ( 45.0) 0.187
MG_BR_amount (%) 0.393
0 2 ( 8.7) 5 ( 25.0)
1 5 ( 21.7) 1 ( 5.0)
2 3 ( 13.0) 3 ( 15.0)
3 7 ( 30.4) 5 ( 25.0)
4 6 ( 26.1) 6 ( 30.0)
MG_BR_rootny = 1 (%) 17 ( 73.9) 8 ( 40.0) 0.053
MG_BR_toyny (%) 0.090
0 16 ( 69.6) 8 ( 40.0)
1 6 ( 26.1) 10 ( 50.0)
4 1 ( 4.3) 0 ( 0.0)
y 0 ( 0.0) 2 ( 10.0)
MG_sickpen_yn = 1 (%) 18 ( 78.3) 15 ( 75.0) 1.000
MG_BR_animdirtmed = 2 (%) 8 ( 36.4) 9 ( 52.9) 0.478
MG_BR_feedtype (%) 0.145
1 1 ( 4.3) 0 ( 0.0)
2 0 ( 0.0) 2 ( 10.0)
3 3 ( 13.0) 0 ( 0.0)
4 18 ( 78.3) 18 ( 90.0)
25 1 ( 4.3) 0 ( 0.0)
MG_BR_feedclean (%) 0.246
0 20 ( 87.0) 15 ( 75.0)
1 2 ( 8.7) 5 ( 25.0)
no 1 ( 4.3) 0 ( 0.0)
MG_BR_calm (%) 0.506
0 1 ( 4.3) 0 ( 0.0)
1 21 ( 91.3) 18 ( 90.0)
2 1 ( 4.3) 2 ( 10.0)
MG_BR_dirtanimmed = 2 (%) 9 ( 42.9) 10 ( 52.6) 0.763
MG_BR_ster = 1 (%) 4 ( 17.4) 2 ( 10.0) 0.798
MG_PR_type (%) 0.295
1 8 ( 34.8) 4 ( 20.0)
2 8 ( 34.8) 10 ( 50.0)
3 0 ( 0.0) 1 ( 5.0)
12 3 ( 13.0) 0 ( 0.0)
13 3 ( 13.0) 2 ( 10.0)
14 1 ( 4.3) 0 ( 0.0)
23 0 ( 0.0) 1 ( 5.0)
123 0 ( 0.0) 1 ( 5.0)
124 0 ( 0.0) 1 ( 5.0)
MG_PR_rootyn = 1 (%) 20 ( 87.0) 16 ( 80.0) 0.840
MG_PR_toyyn = 1 (%) 7 ( 30.4) 9 ( 45.0) 0.503
MG_PR_toy (%) 0.270
0 16 ( 69.6) 11 ( 55.0)
1 0 ( 0.0) 1 ( 5.0)
2 3 ( 13.0) 0 ( 0.0)
3 0 ( 0.0) 1 ( 5.0)
4 4 ( 17.4) 4 ( 20.0)
5 0 ( 0.0) 1 ( 5.0)
14 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
MG_rootamount (%) 0.626
0 3 ( 13.0) 4 ( 20.0)
1 10 ( 43.5) 6 ( 30.0)
2 10 ( 43.5) 10 ( 50.0)
MG_PR_kuivaliete (%) 0.133
1 7 ( 30.4) 6 ( 30.0)
2 12 ( 52.2) 14 ( 70.0)
12 4 ( 17.4) 0 ( 0.0)
MG_PR_ruok_0nonlock_1lock (%) 0.248
0 10 ( 43.5) 12 ( 60.0)
1 13 ( 56.5) 7 ( 35.0)
3 0 ( 0.0) 1 ( 5.0)
MG_PR_feedtype (%) 0.354
1 2 ( 8.7) 1 ( 5.0)
2 1 ( 4.3) 4 ( 20.0)
3 4 ( 17.4) 4 ( 20.0)
4 13 ( 56.5) 7 ( 35.0)
5 1 ( 4.3) 3 ( 15.0)
6 0 ( 0.0) 1 ( 5.0)
25 1 ( 4.3) 0 ( 0.0)
34 1 ( 4.3) 0 ( 0.0)
MG_PR_calm = 2 (%) 1 ( 4.3) 2 ( 10.0) 0.900
MG_PR_animdirtmed = 2 (%) 6 ( 26.1) 9 ( 47.4) 0.267
MG_PR_ster = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
MG_FAR_bed_yn = 1 (%) 17 ( 73.9) 16 ( 80.0) 0.913
MG_FAR_bed12345_NO (%) 0.374
0 6 ( 26.1) 4 ( 20.0)
1 2 ( 8.7) 1 ( 5.0)
2 4 ( 17.4) 7 ( 35.0)
4 1 ( 4.3) 0 ( 0.0)
5 1 ( 4.3) 0 ( 0.0)
12 6 ( 26.1) 4 ( 20.0)
14 2 ( 8.7) 0 ( 0.0)
15 1 ( 4.3) 0 ( 0.0)
24 0 ( 0.0) 1 ( 5.0)
25 0 ( 0.0) 2 ( 10.0)
245 0 ( 0.0) 1 ( 5.0)
MG_FAR_bedamount (%) 0.150
0 0 ( 0.0) 1 ( 5.0)
1 4 ( 17.4) 1 ( 5.0)
2 3 ( 13.0) 8 ( 40.0)
3 9 ( 39.1) 7 ( 35.0)
4 7 ( 30.4) 3 ( 15.0)
MG_FAR_root_yn = 1 (%) 18 ( 78.3) 17 ( 85.0) 0.862
MG_FAR_toy = 1 (%) 11 ( 47.8) 12 ( 60.0) 0.623
MG_FAR_rootamount (%) 0.263
0 2 ( 8.7) 3 ( 15.0)
1 5 ( 21.7) 1 ( 5.0)
2 16 ( 69.6) 16 ( 80.0)
MG_FAR_dirtmed = 2 (%) 7 ( 33.3) 8 ( 47.1) 0.598
MG_FAR_diranimmed = 2 (%) 9 ( 45.0) 8 ( 44.4) 1.000
MG_FAR_toytoinen_MIKA_NO (%) 0.525
2 ( 8.7) 0 ( 0.0)
0 1 ( 4.3) 1 ( 5.0)
1 6 ( 26.1) 3 ( 15.0)
2 11 ( 47.8) 14 ( 70.0)
3 2 ( 8.7) 2 ( 10.0)
noinfo 1 ( 4.3) 0 ( 0.0)
MG_SOWSperworkeredit_NUM57_113_147_NO (%) 0.455
11 1 ( 4.3) 0 ( 0.0)
20 1 ( 4.3) 0 ( 0.0)
23 1 ( 4.3) 0 ( 0.0)
25 0 ( 0.0) 1 ( 5.0)
26 1 ( 4.3) 0 ( 0.0)
29 1 ( 4.3) 0 ( 0.0)
38 0 ( 0.0) 1 ( 5.0)
46 1 ( 4.3) 0 ( 0.0)
49 0 ( 0.0) 1 ( 5.0)
56 1 ( 4.3) 0 ( 0.0)
57 1 ( 4.3) 1 ( 5.0)
62 0 ( 0.0) 1 ( 5.0)
85 0 ( 0.0) 1 ( 5.0)
86 1 ( 4.3) 0 ( 0.0)
88 2 ( 8.7) 1 ( 5.0)
101 1 ( 4.3) 0 ( 0.0)
106 1 ( 4.3) 0 ( 0.0)
113 2 ( 8.7) 0 ( 0.0)
116 0 ( 0.0) 1 ( 5.0)
117 1 ( 4.3) 0 ( 0.0)
126 0 ( 0.0) 1 ( 5.0)
129 1 ( 4.3) 0 ( 0.0)
131 0 ( 0.0) 1 ( 5.0)
132 1 ( 4.3) 0 ( 0.0)
133 0 ( 0.0) 1 ( 5.0)
134 1 ( 4.3) 0 ( 0.0)
135 0 ( 0.0) 1 ( 5.0)
137 0 ( 0.0) 1 ( 5.0)
158 1 ( 4.3) 0 ( 0.0)
163 0 ( 0.0) 1 ( 5.0)
165 0 ( 0.0) 1 ( 5.0)
167 1 ( 4.3) 0 ( 0.0)
181 1 ( 4.3) 0 ( 0.0)
185 0 ( 0.0) 1 ( 5.0)
192 0 ( 0.0) 1 ( 5.0)
196 0 ( 0.0) 1 ( 5.0)
340 0 ( 0.0) 1 ( 5.0)
342 1 ( 4.3) 0 ( 0.0)
395 0 ( 0.0) 1 ( 5.0)
MG_SOWSperworkeredit_57_113_147_ (%) 0.253
1 8 ( 34.8) 4 ( 20.0)
2 7 ( 30.4) 3 ( 15.0)
3 5 ( 21.7) 7 ( 35.0)
4 3 ( 13.0) 6 ( 30.0)
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 ( 30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 ( 47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
MG_BR_giltpurchage_NUM_NO (mean (sd)) 3.45 (2.74) 1.95 (1.88) 0.043
MG_BR_heatdetec_startNUM_NO (mean (sd)) 2.91 (2.20) 4.00 (2.49) 0.135
MG_BR_farmsemenNUM_NO (mean (sd)) 1.36 (1.05) 2.62 (2.52) 0.037
MG_BR_insonceNUM_NO (mean (sd)) 5.86 (4.16) 4.14 (3.64) 0.157
MG_FAR_ToFarunitNUM_NO (mean (sd)) 2.77 (1.38) 3.38 (1.50) 0.173
MG_FAR_NestmatdaysNUM_NO (mean (sd)) 4.55 (2.09) 4.24 (2.41) 0.656
MG_FAR_piglet_rem_ageNUM_NO (mean (sd)) 3.64 (1.62) 3.24 (2.10) 0.488
MG_FAR_pigletremaount_NUM_NO (mean (sd)) 11.64 (6.24) 12.57 (7.40) 0.656
MG_BR_dirt_NUM_NO (mean (sd)) 2.26 (1.19) 2.55 (1.79) 0.562
MG_BR_dirtanim_NUM_NO (mean (sd)) 2.80 (1.28) 2.50 (1.50) 0.501
MG_PR_dirt_NUM_NO (mean (sd)) 3.33 (1.93) 3.81 (2.50) 0.494
MG_PR_late_HAR_kaNUM_NO (mean (sd)) 6.41 (4.96) 4.29 (5.11) 0.174
MG_FAR_toynum_NO (mean (sd)) 3.85 (1.95) 4.27 (1.67) 0.544
MG_FAR_dirt_NUM_NO (mean (sd)) 2.16 (1.26) 2.58 (1.61) 0.375
MG_FAR_diranim_NUM_NO (mean (sd)) 2.63 (1.54) 3.21 (2.15) 0.346
MG_BR_sowsperboar_NUM_NO (mean (sd)) 18.41 (10.39) 18.95 (10.89) 0.868
MG_SOWSperworker_NUM_NO (mean (sd)) 16.71 (10.34) 20.40 (9.64) 0.245
MG_PR_earlyHAR_kaNUM (mean (sd)) 8.59 (4.56) 9.33 (5.74) 0.640
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
MG_feedtimes_preg (%) 0.277
1 0 ( 0.0) 2 ( 10.5)
2 20 (90.9) 14 ( 73.7)
3 2 ( 9.1) 2 ( 10.5)
4 0 ( 0.0) 1 ( 5.3)
MG_feedtimes_far (%) 0.035
2 4 (18.2) 0 ( 0.0)
3 12 (54.5) 17 ( 89.5)
4 6 (27.3) 2 ( 10.5)
MG_feed_liq_solid (%) 0.531
1 ( 4.5) 2 ( 9.5)
liq 12 (54.5) 12 ( 57.1)
liqsol 0 ( 0.0) 2 ( 9.5)
sol 7 (31.8) 4 ( 19.0)
solid 1 ( 4.5) 1 ( 4.8)
solliq 1 ( 4.5) 0 ( 0.0)
MG_owngilts (%) 0.275
0 8 (36.4) 7 ( 33.3)
50 1 ( 4.5) 0 ( 0.0)
70 1 ( 4.5) 0 ( 0.0)
80 1 ( 4.5) 1 ( 4.8)
90 3 (13.6) 0 ( 0.0)
95 0 ( 0.0) 2 ( 9.5)
100 8 (36.4) 11 ( 52.4)
MG_owngilts.1 = 1 (%) 14 (63.6) 14 ( 66.7) 1.000
MG_BR_giltchangebeforeins_NO = 1 (%) 8 (36.4) 11 ( 52.4) 0.453
MG_BR_giltflush_NO (%) 0.128
0 15 (68.2) 8 ( 38.1)
1 6 (27.3) 12 ( 57.1)
noneed 1 ( 4.5) 1 ( 4.8)
MG_BR_giltboarstart_NO (%) 0.530
0 2 ( 9.1) 2 ( 9.5)
3 1 ( 4.5) 0 ( 0.0)
4 1 ( 4.5) 0 ( 0.0)
6 5 (22.7) 3 ( 14.3)
6,5 1 ( 4.5) 2 ( 9.5)
7 9 (40.9) 7 ( 33.3)
7,5 2 ( 9.1) 6 ( 28.6)
8 1 ( 4.5) 0 ( 0.0)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_giltinsage_NO (%) 0.771
0 2 ( 9.1) 1 ( 4.8)
2ndheat 1 ( 4.5) 0 ( 0.0)
6 1 ( 4.5) 0 ( 0.0)
7 1 ( 4.5) 1 ( 4.8)
7,5 1 ( 4.5) 1 ( 4.8)
8 12 (54.5) 13 ( 61.9)
8,5 1 ( 4.5) 3 ( 14.3)
9 1 ( 4.5) 0 ( 0.0)
9,5 2 ( 9.1) 1 ( 4.8)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_heatgroup_NO (%) 0.426
0 3 (13.6) 6 ( 28.6)
1 18 (81.8) 13 ( 61.9)
no 0 ( 0.0) 1 ( 4.8)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_BR_heatmarkback_NO (%) 0.044
0 9 (40.9) 2 ( 9.5)
1 13 (59.1) 18 ( 85.7)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_BR_artinspro_050_5099_100 (%) 0.666
0 1 ( 4.5) 1 ( 4.8)
1 8 (36.4) 5 ( 23.8)
2 13 (59.1) 15 ( 71.4)
MG_BR_once_012 (%) 0.894
0 7 (33.3) 8 ( 40.0)
1 12 (57.1) 10 ( 50.0)
2 2 ( 9.5) 2 ( 10.0)
MG_BR_instriple_NO (%) 0.432
0 7 (31.8) 4 ( 19.0)
1 2 ( 9.1) 1 ( 4.8)
10 4 (18.2) 7 ( 33.3)
15 0 ( 0.0) 2 ( 9.5)
2 1 ( 4.5) 0 ( 0.0)
3 2 ( 9.1) 0 ( 0.0)
30 0 ( 0.0) 1 ( 4.8)
33 0 ( 0.0) 1 ( 4.8)
5 5 (22.7) 4 ( 19.0)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_BR_triple_012 (%) 0.175
0 7 (31.8) 4 ( 19.0)
1 14 (63.6) 12 ( 57.1)
2 0 ( 0.0) 4 ( 19.0)
noinfo 1 ( 4.5) 1 ( 4.8)
MG_aveins (%) 0.367
1 1 ( 4.5) 0 ( 0.0)
2 21 (95.5) 20 ( 95.2)
2,1 0 ( 0.0) 1 ( 4.8)
MG_BR_nopregus (%) 0.099
0 7 (31.8) 6 ( 28.6)
1 14 (63.6) 8 ( 38.1)
2 1 ( 4.5) 6 ( 28.6)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_FAR_ind_0no_1rout_2sometimes (%) 0.164
0 11 (50.0) 6 ( 28.6)
1 0 ( 0.0) 2 ( 9.5)
2 11 (50.0) 13 ( 61.9)
MG_FAR_nestmatamount (%) 0.038
0 2 ( 9.1) 3 ( 14.3)
1 4 (18.2) 0 ( 0.0)
2 14 (63.6) 10 ( 47.6)
3 2 ( 9.1) 8 ( 38.1)
MG_FAR_nestmat_NO (%) 0.210
_CUT 2 ( 9.1) 0 ( 0.0)
_NWS 2 ( 9.1) 3 ( 14.3)
0 2 ( 9.1) 3 ( 14.3)
heiina 1 ( 4.5) 0 ( 0.0)
heina 0 ( 0.0) 1 ( 4.8)
heina puruturve 0 ( 0.0) 1 ( 4.8)
heina turve 1 ( 4.5) 0 ( 0.0)
no 0 ( 0.0) 1 ( 4.8)
STR 11 (50.0) 6 ( 28.6)
STR_CUT 1 ( 4.5) 6 ( 28.6)
STR_CUT_NWS 1 ( 4.5) 0 ( 0.0)
STR_NWS 1 ( 4.5) 0 ( 0.0)
MG_FAR_ox_0_13_46_7 (%) 0.652
0 1 ( 4.5) 0 ( 0.0)
1 9 (40.9) 9 ( 45.0)
2 4 (18.2) 2 ( 10.0)
3 8 (36.4) 9 ( 45.0)
MG_FAR_obstex_preox (%) 0.541
0 13 (59.1) 13 ( 61.9)
1 9 (40.9) 7 ( 33.3)
noinfo 0 ( 0.0) 1 ( 4.8)
MG_FAR_far_assist_CAT (%) 0.289
<6 6 (27.3) 5 ( 23.8)
10 0 ( 0.0) 1 ( 4.8)
15 1 ( 4.5) 0 ( 0.0)
20-50 4 (18.2) 4 ( 19.0)
50 4 (18.2) 0 ( 0.0)
6-20 6 (27.3) 8 ( 38.1)
noinfo 1 ( 4.5) 3 ( 14.3)
MG_FAR_farassist_MAY_NO (%) 0.463
0 ( 0.0) 2 ( 9.5)
_GLO_LUBR 9 (40.9) 6 ( 28.6)
_HANDWASH_GLO_LUBR 0 ( 0.0) 1 ( 4.8)
GLO_LUBR 0 ( 0.0) 1 ( 4.8)
WASH_GLO_LUBR 6 (27.3) 7 ( 33.3)
WASH_HANDWASH_GLO_LUBR 6 (27.3) 3 ( 14.3)
WASH_HANDWASH_LUBR 1 ( 4.5) 1 ( 4.8)
MG_FAR_piglet_rem_amountCAT (%) 0.088
0 0 ( 0.0) 1 ( 4.8)
1 10 (45.5) 5 ( 23.8)
2 6 (27.3) 5 ( 23.8)
3 5 (22.7) 2 ( 9.5)
4 1 ( 4.5) 6 ( 28.6)
noinfo 0 ( 0.0) 2 ( 9.5)
MG_FAR_piglet_addfeedage (%) 0.530
<3 0 ( 0.0) 1 ( 4.8)
<7 12 (54.5) 10 ( 47.6)
>20 1 ( 4.5) 0 ( 0.0)
7-14 9 (40.9) 10 ( 47.6)
MG_ind_feed = 1 (%) 16 (72.7) 15 ( 71.4) 1.000
MG_BR_bedtype_NO (%) 0.529
0 8 (36.4) 10 ( 47.6)
1 6 (27.3) 5 ( 23.8)
2 4 (18.2) 1 ( 4.8)
5 0 ( 0.0) 1 ( 4.8)
12 1 ( 4.5) 3 ( 14.3)
14 1 ( 4.5) 1 ( 4.8)
25 1 ( 4.5) 0 ( 0.0)
125 1 ( 4.5) 0 ( 0.0)
MG_BR_bedny = 1 (%) 14 (63.6) 11 ( 52.4) 0.661
MG_BR_amount (%) 0.294
0 2 ( 9.1) 5 ( 23.8)
1 2 ( 9.1) 4 ( 19.0)
2 5 (22.7) 1 ( 4.8)
3 7 (31.8) 5 ( 23.8)
4 6 (27.3) 6 ( 28.6)
MG_BR_rootny = 1 (%) 14 (63.6) 11 ( 52.4) 0.661
MG_BR_toyny (%) 0.450
0 14 (63.6) 10 ( 47.6)
1 6 (27.3) 10 ( 47.6)
4 1 ( 4.5) 0 ( 0.0)
y 1 ( 4.5) 1 ( 4.8)
MG_sickpen_yn = 1 (%) 18 (81.8) 15 ( 71.4) 0.656
MG_BR_animdirtmed = 2 (%) 8 (42.1) 9 ( 45.0) 1.000
MG_BR_feedtype (%) 0.115
1 1 ( 4.5) 0 ( 0.0)
2 2 ( 9.1) 0 ( 0.0)
3 3 (13.6) 0 ( 0.0)
4 16 (72.7) 20 ( 95.2)
25 0 ( 0.0) 1 ( 4.8)
MG_BR_feedclean (%) 0.563
0 18 (81.8) 17 ( 81.0)
1 3 (13.6) 4 ( 19.0)
no 1 ( 4.5) 0 ( 0.0)
MG_BR_calm (%) 0.513
0 0 ( 0.0) 1 ( 4.8)
1 20 (90.9) 19 ( 90.5)
2 2 ( 9.1) 1 ( 4.8)
MG_BR_dirtanimmed = 2 (%) 11 (55.0) 8 ( 40.0) 0.527
MG_BR_ster = 1 (%) 2 ( 9.1) 4 ( 19.0) 0.616
MG_PR_type (%) 0.640
1 5 (22.7) 7 ( 33.3)
2 10 (45.5) 8 ( 38.1)
3 0 ( 0.0) 1 ( 4.8)
12 1 ( 4.5) 2 ( 9.5)
13 3 (13.6) 2 ( 9.5)
14 1 ( 4.5) 0 ( 0.0)
23 1 ( 4.5) 0 ( 0.0)
123 0 ( 0.0) 1 ( 4.8)
124 1 ( 4.5) 0 ( 0.0)
MG_PR_rootyn = 1 (%) 20 (90.9) 16 ( 76.2) 0.372
MG_PR_toyyn = 1 (%) 6 (27.3) 10 ( 47.6) 0.287
MG_PR_toy (%) 0.456
0 16 (72.7) 11 ( 52.4)
1 0 ( 0.0) 1 ( 4.8)
2 1 ( 4.5) 2 ( 9.5)
3 0 ( 0.0) 1 ( 4.8)
4 3 (13.6) 5 ( 23.8)
5 1 ( 4.5) 0 ( 0.0)
14 0 ( 0.0) 1 ( 4.8)
24 1 ( 4.5) 0 ( 0.0)
MG_rootamount (%) 0.356
0 2 ( 9.1) 5 ( 23.8)
1 8 (36.4) 8 ( 38.1)
2 12 (54.5) 8 ( 38.1)
MG_PR_kuivaliete (%) 0.663
1 8 (36.4) 5 ( 23.8)
2 12 (54.5) 14 ( 66.7)
12 2 ( 9.1) 2 ( 9.5)
MG_PR_ruok_0nonlock_1lock (%) 0.560
0 12 (54.5) 10 ( 47.6)
1 10 (45.5) 10 ( 47.6)
3 0 ( 0.0) 1 ( 4.8)
MG_PR_feedtype (%) 0.646
1 1 ( 4.5) 2 ( 9.5)
2 4 (18.2) 1 ( 4.8)
3 4 (18.2) 4 ( 19.0)
4 10 (45.5) 10 ( 47.6)
5 2 ( 9.1) 2 ( 9.5)
6 0 ( 0.0) 1 ( 4.8)
25 0 ( 0.0) 1 ( 4.8)
34 1 ( 4.5) 0 ( 0.0)
MG_PR_calm = 2 (%) 0 ( 0.0) 3 ( 14.3) 0.215
MG_PR_animdirtmed = 2 (%) 6 (28.6) 9 ( 42.9) 0.520
MG_PR_ster = 1 (%) 2 ( 9.1) 2 ( 9.5) 1.000
MG_FAR_bed_yn = 1 (%) 18 (81.8) 15 ( 71.4) 0.656
MG_FAR_bed12345_NO (%) 0.798
0 4 (18.2) 6 ( 28.6)
1 2 ( 9.1) 1 ( 4.8)
2 5 (22.7) 6 ( 28.6)
4 0 ( 0.0) 1 ( 4.8)
5 1 ( 4.5) 0 ( 0.0)
12 6 (27.3) 4 ( 19.0)
14 1 ( 4.5) 1 ( 4.8)
15 1 ( 4.5) 0 ( 0.0)
24 0 ( 0.0) 1 ( 4.8)
25 1 ( 4.5) 1 ( 4.8)
245 1 ( 4.5) 0 ( 0.0)
MG_FAR_bedamount (%) 0.538
0 0 ( 0.0) 1 ( 4.8)
1 4 (18.2) 1 ( 4.8)
2 6 (27.3) 5 ( 23.8)
3 7 (31.8) 9 ( 42.9)
4 5 (22.7) 5 ( 23.8)
MG_FAR_root_yn = 1 (%) 19 (86.4) 16 ( 76.2) 0.642
MG_FAR_toy = 1 (%) 10 (45.5) 13 ( 61.9) 0.438
MG_FAR_rootamount (%) 0.277
0 1 ( 4.5) 4 ( 19.0)
1 4 (18.2) 2 ( 9.5)
2 17 (77.3) 15 ( 71.4)
MG_FAR_dirtmed = 2 (%) 7 (36.8) 8 ( 42.1) 1.000
MG_FAR_diranimmed = 2 (%) 7 (36.8) 10 ( 52.6) 0.514
MG_FAR_toytoinen_MIKA_NO (%) 0.801
1 ( 4.5) 1 ( 4.8)
0 1 ( 4.5) 1 ( 4.8)
1 3 (13.6) 6 ( 28.6)
2 14 (63.6) 11 ( 52.4)
3 2 ( 9.1) 2 ( 9.5)
noinfo 1 ( 4.5) 0 ( 0.0)
MG_SOWSperworkeredit_NUM57_113_147_NO (%) 0.547
11 1 ( 4.5) 0 ( 0.0)
20 1 ( 4.5) 0 ( 0.0)
23 0 ( 0.0) 1 ( 4.8)
25 0 ( 0.0) 1 ( 4.8)
26 1 ( 4.5) 0 ( 0.0)
29 1 ( 4.5) 0 ( 0.0)
38 1 ( 4.5) 0 ( 0.0)
46 1 ( 4.5) 0 ( 0.0)
49 1 ( 4.5) 0 ( 0.0)
56 1 ( 4.5) 0 ( 0.0)
57 1 ( 4.5) 1 ( 4.8)
62 1 ( 4.5) 0 ( 0.0)
85 1 ( 4.5) 0 ( 0.0)
86 0 ( 0.0) 1 ( 4.8)
88 1 ( 4.5) 2 ( 9.5)
101 0 ( 0.0) 1 ( 4.8)
106 0 ( 0.0) 1 ( 4.8)
113 1 ( 4.5) 1 ( 4.8)
116 1 ( 4.5) 0 ( 0.0)
117 0 ( 0.0) 1 ( 4.8)
126 1 ( 4.5) 0 ( 0.0)
129 1 ( 4.5) 0 ( 0.0)
131 0 ( 0.0) 1 ( 4.8)
132 0 ( 0.0) 1 ( 4.8)
133 1 ( 4.5) 0 ( 0.0)
134 1 ( 4.5) 0 ( 0.0)
135 1 ( 4.5) 0 ( 0.0)
137 1 ( 4.5) 0 ( 0.0)
158 1 ( 4.5) 0 ( 0.0)
163 0 ( 0.0) 1 ( 4.8)
165 0 ( 0.0) 1 ( 4.8)
167 0 ( 0.0) 1 ( 4.8)
181 1 ( 4.5) 0 ( 0.0)
185 0 ( 0.0) 1 ( 4.8)
192 0 ( 0.0) 1 ( 4.8)
196 0 ( 0.0) 1 ( 4.8)
340 0 ( 0.0) 1 ( 4.8)
342 0 ( 0.0) 1 ( 4.8)
395 0 ( 0.0) 1 ( 4.8)
MG_SOWSperworkeredit_57_113_147_ (%) 0.017
1 9 (40.9) 3 ( 14.3)
2 4 (18.2) 6 ( 28.6)
3 8 (36.4) 4 ( 19.0)
4 1 ( 4.5) 8 ( 38.1)
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(52,53),quali.sup=c(50:51), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(52, 53), quali.sup = c(50:51),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.225   0.155   0.143   0.137   0.128   0.115
## % of var.              8.364   5.749   5.308   5.097   4.748   4.278
## Cumulative % of var.   8.364  14.112  19.420  24.517  29.265  33.543
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.113   0.102   0.098   0.089   0.086   0.086
## % of var.              4.179   3.782   3.620   3.300   3.205   3.177
## Cumulative % of var.  37.722  41.505  45.124  48.425  51.629  54.806
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.081   0.077   0.072   0.070   0.067   0.064
## % of var.              3.015   2.856   2.683   2.591   2.474   2.388
## Cumulative % of var.  57.821  60.677  63.360  65.950  68.425  70.813
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.062   0.055   0.054   0.052   0.050   0.047
## % of var.              2.313   2.060   2.012   1.931   1.839   1.754
## Cumulative % of var.  73.126  75.186  77.198  79.129  80.969  82.723
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.046   0.044   0.038   0.038   0.035   0.032
## % of var.              1.692   1.619   1.423   1.412   1.316   1.178
## Cumulative % of var.  84.415  86.034  87.457  88.869  90.184  91.363
##                       Dim.31  Dim.32  Dim.33  Dim.34  Dim.35  Dim.36
## Variance               0.029   0.029   0.027   0.024   0.021   0.020
## % of var.              1.069   1.059   1.003   0.897   0.793   0.749
## Cumulative % of var.  92.432  93.491  94.494  95.390  96.183  96.932
##                       Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               0.019   0.018   0.014   0.013   0.012   0.008
## % of var.              0.689   0.663   0.530   0.468   0.431   0.286
## Cumulative % of var.  97.622  98.285  98.815  99.283  99.714 100.000
## 
## Individuals (the 10 first)
##                                   Dim.1    ctr   cos2    Dim.2    ctr
## 1                              |  0.317  1.037  0.044 |  0.002  0.000
## 2                              | -0.440  1.999  0.089 | -0.074  0.083
## 3                              | -0.508  2.667  0.068 | -0.237  0.847
## 4                              |  0.215  0.478  0.031 | -0.258  1.001
## 5                              |  0.690  4.913  0.145 | -0.085  0.107
## 6                              | -0.465  2.234  0.077 | -0.233  0.814
## 7                              | -0.171  0.301  0.012 |  0.077  0.088
## 8                              | -0.037  0.014  0.001 | -0.218  0.714
## 9                              | -0.210  0.455  0.016 |  0.019  0.005
## 10                             |  0.142  0.207  0.010 | -0.466  3.257
##                                  cos2    Dim.3    ctr   cos2  
## 1                               0.000 | -0.213  0.741  0.020 |
## 2                               0.003 | -0.100  0.163  0.005 |
## 3                               0.015 | -0.615  6.152  0.100 |
## 4                               0.045 | -0.226  0.827  0.034 |
## 5                               0.002 |  0.140  0.320  0.006 |
## 6                               0.019 | -0.304  1.499  0.033 |
## 7                               0.002 | -0.156  0.394  0.010 |
## 8                               0.024 |  0.016  0.004  0.000 |
## 9                               0.000 | -0.059  0.057  0.001 |
## 10                              0.104 | -0.290  1.372  0.040 |
## 
## Categories (the 10 first)
##                                   Dim.1    ctr   cos2 v.test    Dim.2
## MG_feedtimes_preg_1            | -0.345  0.050  0.006 -0.493 |  0.169
## MG_feedtimes_preg_2            |  0.028  0.006  0.003  0.358 | -0.120
## MG_feedtimes_preg_3            | -0.275  0.064  0.008 -0.570 |  0.663
## MG_feedtimes_preg_4            | -0.737  0.114  0.013 -0.737 |  1.163
## MG_feedtimes_preg_Not Assigned |  0.779  0.255  0.030  1.115 | -0.034
## MG_feedtimes_far_2             | -0.706  0.421  0.051 -1.466 | -0.921
## MG_feedtimes_far_3             |  0.012  0.001  0.000  0.115 |  0.100
## MG_feedtimes_far_4             |  0.114  0.022  0.003  0.352 |  0.106
## MG_feedtimes_far_Not Assigned  |  0.779  0.255  0.030  1.115 | -0.034
## V10                            |  0.619  0.242  0.029  1.098 | -0.417
##                                   ctr   cos2 v.test    Dim.3    ctr   cos2
## MG_feedtimes_preg_1             0.018  0.001  0.242 | -1.191  0.942  0.069
## MG_feedtimes_preg_2             0.150  0.055 -1.514 |  0.014  0.002  0.001
## MG_feedtimes_preg_3             0.539  0.045  1.376 | -0.151  0.030  0.002
## MG_feedtimes_preg_4             0.415  0.032  1.163 |  0.585  0.114  0.008
## MG_feedtimes_preg_Not Assigned  0.001  0.000 -0.048 |  0.960  0.612  0.045
## MG_feedtimes_far_2              1.040  0.087 -1.912 |  1.090  1.576  0.122
## MG_feedtimes_far_3              0.089  0.021  0.935 | -0.144  0.198  0.043
## MG_feedtimes_far_4              0.027  0.003  0.328 | -0.264  0.186  0.016
## MG_feedtimes_far_Not Assigned   0.001  0.000 -0.048 |  0.960  0.612  0.045
## V10                             0.160  0.013 -0.740 |  0.384  0.147  0.011
##                                v.test  
## MG_feedtimes_preg_1            -1.705 |
## MG_feedtimes_preg_2             0.179 |
## MG_feedtimes_preg_3            -0.314 |
## MG_feedtimes_preg_4             0.585 |
## MG_feedtimes_preg_Not Assigned  1.374 |
## MG_feedtimes_far_2              2.261 |
## MG_feedtimes_far_3             -1.339 |
## MG_feedtimes_far_4             -0.819 |
## MG_feedtimes_far_Not Assigned   1.374 |
## V10                             0.681 |
## 
## Categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## MG_feedtimes_preg              | 0.054 0.085 0.119 |
## MG_feedtimes_far               | 0.077 0.088 0.180 |
## MG_feed_liq_solid              | 0.180 0.125 0.241 |
## MG_owngilts                    | 0.227 0.356 0.168 |
## MG_owngilts.1                  | 0.155 0.239 0.018 |
## MG_BR_artinspro_050_5099_100   | 0.274 0.339 0.050 |
## MG_BR_once_012                 | 0.336 0.183 0.168 |
## MG_BR_triple_012               | 0.412 0.095 0.042 |
## MG_aveins                      | 0.067 0.411 0.019 |
## MG_BR_nopregus                 | 0.375 0.024 0.147 |
## 
## Supplementary categories
##                                   Dim.1   cos2 v.test    Dim.2   cos2
## OUT_SOW_totrem_dic_0           | -0.330  0.104 -2.091 | -0.330  0.104
## OUT_SOW_totrem_dic_1           |  0.315  0.104  2.091 |  0.315  0.104
## OUT_SOW_cull_dic_0             | -0.292  0.089 -1.937 | -0.048  0.002
## OUT_SOW_cull_dic_1             |  0.306  0.089  1.937 |  0.051  0.002
##                                v.test    Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0           -2.091 |  0.100  0.009  0.631 |
## OUT_SOW_totrem_dic_1            2.091 | -0.095  0.009 -0.631 |
## OUT_SOW_cull_dic_0             -0.320 |  0.119  0.015  0.790 |
## OUT_SOW_cull_dic_1              0.320 | -0.125  0.015 -0.790 |
## 
## Supplementary categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic             | 0.104 0.104 0.009 |
## OUT_SOW_cull_dic               | 0.089 0.002 0.015 |
## 
## Supplementary continuous variables
##                                   Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM            |  0.304 |  0.285 | -0.090 |
## OUT_SOW_cullproNUM             |  0.301 |  0.188 | -0.154 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("28", "24", "13", "21", "14", "42", "27", "39", "29", "41")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 4 clusters.*
## 
## 
## The 1st cluster is made of individuals such as *28*. This group is characterized by :
## 
## - high frequency for factors like *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__1*, *MG_BR_nopregus=MG_BR_nopregus_0*, *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_<6*, *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_0*, *MG_BR_rootny=MG_BR_rootny_1*, *MG_BR_bedny=MG_BR_bedny_1*, *MG_BR_feedtype=MG_BR_feedtype_2*, *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_0*, *MG_FAR_bedamount=MG_FAR_bedamount_1* and *MG_PR_feedtype=MG_PR_feedtype_2* (factors are sorted from the most common).
## - low frequency for the factors *MG_BR_feedtype=MG_BR_feedtype_4*, *MG_BR_bedny=MG_BR_bedny_0*, *MG_BR_rootny=MG_BR_rootny_0*, *MG_FAR_ox_0_13_46_7=MG_FAR_ox_0_13_46_7_3*, *MG_FAR_ind_0no_1rout_2sometimes=MG_FAR_ind_0no_1rout_2sometimes_2*, *MG_BR_nopregus=MG_BR_nopregus_1*, *MG_FAR_piglet_addfeedage=<7* and *MG_BR_artinspro_050_5099_100=MG_BR_artinspro_050_5099_100_2* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by14* and *14*. :
## 
## - high frequency for factors like *MG_PR_toy=MG_PR_toy_0*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_FAR_toy=MG_FAR_toy_0*, *MG_PR_feedtype=MG_PR_feedtype_4*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_1*, *MG_PR_type=MG_PR_type_1*, *MG_FAR_bedamount=MG_FAR_bedamount_3*, *MG_rootamount=MG_rootamount_1*, *MG_BR_amount=MG_BR_amount_1* and *MG_PR_kuivaliete=MG_PR_kuivaliete_1* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_type=MG_PR_type_2*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_FAR_toy=MG_FAR_toy_1*, *MG_PR_ruok_0nonlock_1lock=MG_PR_ruok_0nonlock_1lock_0*, *MG_PR_kuivaliete=MG_PR_kuivaliete_2*, *MG_PR_feedtype=MG_PR_feedtype_3*, *MG_PR_animdirtmed=MG_PR_animdirtmed_2*, *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__4*, *MG_PR_feedtype=MG_PR_feedtype_2* and *MG_PR_toy=MG_PR_toy_4* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by29* and *29*. :
## 
## - high frequency for factors like *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__4*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_4*, *MG_PR_type=MG_PR_type_2*, *MG_FAR_bed_yn=MG_FAR_bed_yn_0*, *MG_PR_toyyn=MG_PR_toyyn_1*, *MG_BR_ster=MG_BR_ster_1*, *MG_FAR_toy=MG_FAR_toy_1*, *MG_BR_bedny=MG_BR_bedny_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* and *MG_FAR_bedamount=MG_FAR_bedamount_4* (factors are sorted from the most common).
## - low frequency for factors like *MG_FAR_bed_yn=MG_FAR_bed_yn_1*, *MG_SOWSperworkeredit_57_113_147_=MG_SOWSperworkeredit_57_113_147__1*, *MG_PR_toyyn=MG_PR_toyyn_0*, *MG_PR_toy=MG_PR_toy_0*, *MG_BR_ster=MG_BR_ster_0*, *MG_FAR_toy=MG_FAR_toy_0*, *MG_FAR_bedamount=MG_FAR_bedamount_3*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_2*, *MG_BR_animdirtmed=MG_BR_animdirtmed_1* and *MG_BR_bedny=MG_BR_bedny_1* (factors are sorted from the rarest).
## 
## The cluster 4 is made of individuals such as*. This group is characterized by13* and *13*. :
## 
## - high frequency for factors like *MG_FAR_far_assist_CAT=MG_FAR_far_assist_CAT_noinfo*, *MG_rootamount=MG_rootamount_0*, *MG_PR_rootyn=MG_PR_rootyn_0*, *MG_BR_feedclean=MG_BR_feedclean_1*, *MG_FAR_piglet_rem_amountCAT=MG_FAR_piglet_rem_amountCAT_noinfo*, *MG_BR_triple_012=MG_BR_triple_012_noinfo*, *MG_BR_once_012=MG_BR_once_012_Not Assigned*, *MG_sickpen_yn=MG_sickpen_yn_0*, *MG_BR_amount=MG_BR_amount_4* and *MG_FAR_nestmatamount=MG_FAR_nestmatamount_0* (factors are sorted from the most common).
## - low frequency for the factors *MG_PR_rootyn=MG_PR_rootyn_1*, *MG_BR_feedclean=MG_BR_feedclean_0*, *MG_sickpen_yn=MG_sickpen_yn_1* and *MG_PR_animdirtmed=MG_PR_animdirtmed_1* (factors are sorted from the rarest).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$quanti.var" 
## 4  "$desc.var$quanti"     
## 5  "$desc.var$test.chi2"  
## 6  "$desc.axes$category"  
## 7  "$desc.axes"           
## 8  "$desc.axes$quanti.var"
## 9  "$desc.axes$quanti"    
## 10 "$desc.ind"            
## 11 "$desc.ind$para"       
## 12 "$desc.ind$dist"       
## 13 "$call"                
## 14 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the continuous var." 
## 4  "description of the clusters by the continuous var."     
## 5  "description of the cluster var. by the categorical var."
## 6  "description of the clusters by the categories."         
## 7  "description of the clusters by the dimensions"          
## 8  "description of the cluster var. by the axes"            
## 9  "description of the clusters by the axes"                
## 10 "description of the clusters by the individuals"         
## 11 "parangons of each clusters"                             
## 12 "specific individuals"                                   
## 13 "summary statistics"                                     
## 14 "description of the tree"

Housing; breeding unit

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roombr.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 19
## $ R_BR_sowspersection            <fctr> >100, all, all, 50-100, noinfo...
## $ R_BR_area2_NUM                 <fctr> 1,5275, group, 1,536, 1,5675, ...
## $ R_BR_type                      <int> 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3...
## $ R_BR_noise                     <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0...
## $ R_BR_pest_NO                   <int> 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0...
## $ R_BR_airqual                   <int> 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0...
## $ R_BR_C_NUM_NO                  <fctr> 19, 0, 18, 26, 19, 23, 19, 17,...
## $ R_BR_outC                      <fctr> , 8, 15, 25, 24, , , 2, 25, 25...
## $ R_BR_floorbetmetplastwoodother <fctr> bet, bet, bet, betmet, bet, be...
## $ R_BR_floorsolid_NUM_NO         <int> 80, 80, 99, 80, 70, 100, 80, 90...
## $ R_BR_floorsolid_0981_2         <int> 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1...
## $ R_BR_kuivaliete                <int> 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2...
## $ R_BR_PREGsame                  <int> 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1...
## $ OUT_SOW_mort_proNUM            <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, ...
## $ OUT_SOW_mort_dic               <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0...
## $ OUT_SOW_totremproNUM           <int> 34, 38, 53, 57, 65, 64, 47, 44,...
## $ OUT_SOW_totrem_dic             <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0...
## $ OUT_SOW_cullproNUM             <int> 29, 33, 45, 30, 55, 64, 30, 31,...
## $ OUT_SOW_cull_dic               <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "R_BR_PREGsame"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="lightgreen") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
R_BR_C_NUM_NO (mean (sd)) 13.52 (7.56) 15.80 (8.97) 0.371
R_BR_floorsolid_NUM_NO (mean (sd)) 7.87 (3.08) 7.90 (2.02) 0.970
R_BR_area2_NUM (mean (sd)) 18.26 (9.99) 16.10 (8.85) 0.460
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
R_BR_sowspersection (%) 0.509
<20 2 ( 8.7) 1 ( 5.0)
>100 3 (13.0) 3 ( 15.0)
20-50 5 (21.7) 6 ( 30.0)
50-100 9 (39.1) 7 ( 35.0)
all 4 (17.4) 1 ( 5.0)
noinfo 0 ( 0.0) 2 ( 10.0)
R_BR_type (%) 0.334
1 3 (13.0) 1 ( 5.0)
2 1 ( 4.3) 1 ( 5.0)
3 15 (65.2) 16 ( 80.0)
12 2 ( 8.7) 0 ( 0.0)
13 2 ( 8.7) 0 ( 0.0)
23 0 ( 0.0) 1 ( 5.0)
124 0 ( 0.0) 1 ( 5.0)
R_BR_noise = 1 (%) 13 (56.5) 9 ( 45.0) 0.654
R_BR_pest_NO = 1 (%) 10 (43.5) 11 ( 55.0) 0.654
R_BR_airqual = 1 (%) 7 (30.4) 11 ( 55.0) 0.187
R_BR_outC (%) 0.374
13 (56.5) 10 ( 50.0)
10 1 ( 4.3) 0 ( 0.0)
13 1 ( 4.3) 0 ( 0.0)
14 0 ( 0.0) 2 ( 10.0)
15 1 ( 4.3) 0 ( 0.0)
18 1 ( 4.3) 0 ( 0.0)
19 1 ( 4.3) 1 ( 5.0)
2 0 ( 0.0) 1 ( 5.0)
20 0 ( 0.0) 1 ( 5.0)
23 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
25 4 (17.4) 1 ( 5.0)
28,3 0 ( 0.0) 1 ( 5.0)
31 0 ( 0.0) 1 ( 5.0)
8 1 ( 4.3) 0 ( 0.0)
R_BR_floorbetmetplastwoodother = betmet (%) 4 (17.4) 8 ( 40.0) 0.191
R_BR_floorsolid_0981_2 = 2 (%) 5 (21.7) 2 ( 10.0) 0.531
R_BR_kuivaliete (%) 0.769
1 6 (26.1) 4 ( 20.0)
2 15 (65.2) 15 ( 75.0)
12 2 ( 8.7) 1 ( 5.0)
R_BR_PREGsame = 1 (%) 8 (34.8) 4 ( 21.1) 0.524
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
R_BR_C_NUM_NO (mean (sd)) 15.50 (9.06) 13.62 (7.33) 0.460
R_BR_floorsolid_NUM_NO (mean (sd)) 8.09 (2.62) 7.67 (2.65) 0.600
R_BR_area2_NUM (mean (sd)) 18.41 (9.50) 16.05 (9.44) 0.418
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
R_BR_sowspersection (%) 0.821
<20 1 ( 4.5) 2 ( 9.5)
>100 3 (13.6) 3 ( 14.3)
20-50 5 (22.7) 6 ( 28.6)
50-100 8 (36.4) 8 ( 38.1)
all 4 (18.2) 1 ( 4.8)
noinfo 1 ( 4.5) 1 ( 4.8)
R_BR_type (%) 0.542
1 1 ( 4.5) 3 ( 14.3)
2 2 ( 9.1) 0 ( 0.0)
3 16 (72.7) 15 ( 71.4)
12 1 ( 4.5) 1 ( 4.8)
13 1 ( 4.5) 1 ( 4.8)
23 0 ( 0.0) 1 ( 4.8)
124 1 ( 4.5) 0 ( 0.0)
R_BR_noise = 1 (%) 10 (45.5) 12 ( 57.1) 0.645
R_BR_pest_NO = 1 (%) 11 (50.0) 10 ( 47.6) 1.000
R_BR_airqual = 1 (%) 12 (54.5) 6 ( 28.6) 0.157
R_BR_outC (%) 0.366
10 (45.5) 13 ( 61.9)
10 0 ( 0.0) 1 ( 4.8)
13 1 ( 4.5) 0 ( 0.0)
14 1 ( 4.5) 1 ( 4.8)
15 0 ( 0.0) 1 ( 4.8)
18 1 ( 4.5) 0 ( 0.0)
19 0 ( 0.0) 2 ( 9.5)
2 1 ( 4.5) 0 ( 0.0)
20 1 ( 4.5) 0 ( 0.0)
23 1 ( 4.5) 0 ( 0.0)
24 0 ( 0.0) 1 ( 4.8)
25 4 (18.2) 1 ( 4.8)
28,3 0 ( 0.0) 1 ( 4.8)
31 1 ( 4.5) 0 ( 0.0)
8 1 ( 4.5) 0 ( 0.0)
R_BR_floorbetmetplastwoodother = betmet (%) 6 (27.3) 6 ( 28.6) 1.000
R_BR_floorsolid_0981_2 = 2 (%) 3 (13.6) 4 ( 19.0) 0.946
R_BR_kuivaliete (%) 0.185
1 4 (18.2) 6 ( 28.6)
2 15 (68.2) 15 ( 71.4)
12 3 (13.6) 0 ( 0.0)
R_BR_PREGsame = 1 (%) 7 (33.3) 5 ( 23.8) 0.733
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(13,14),quali.sup=c(11:12), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(13, 14), quali.sup = c(11:12),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.398   0.296   0.210   0.199   0.190   0.184
## % of var.             11.709   8.696   6.180   5.843   5.580   5.405
## Cumulative % of var.  11.709  20.405  26.585  32.428  38.007  43.413
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.169   0.155   0.139   0.132   0.128   0.121
## % of var.              4.958   4.554   4.083   3.880   3.756   3.570
## Cumulative % of var.  48.371  52.925  57.008  60.888  64.644  68.214
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.118   0.109   0.107   0.103   0.101   0.096
## % of var.              3.470   3.205   3.135   3.017   2.965   2.834
## Cumulative % of var.  71.684  74.889  78.024  81.040  84.005  86.840
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.087   0.075   0.055   0.052   0.045   0.034
## % of var.              2.545   2.215   1.631   1.539   1.310   0.995
## Cumulative % of var.  89.384  91.599  93.230  94.769  96.080  97.075
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.025   0.024   0.020   0.016   0.008   0.004
## % of var.              0.737   0.698   0.603   0.468   0.221   0.131
## Cumulative % of var.  97.811  98.509  99.112  99.580  99.801  99.932
##                       Dim.31  Dim.32  Dim.33  Dim.34
## Variance               0.002   0.000   0.000   0.000
## % of var.              0.054   0.014   0.000   0.000
## Cumulative % of var.  99.986 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                         Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                    | -0.234  0.319  0.045 |  0.021  0.004  0.000 |
## 2                    |  0.977  5.574  0.144 | -0.119  0.111  0.002 |
## 3                    |  1.357 10.752  0.287 |  0.326  0.836  0.017 |
## 4                    | -0.601  2.113  0.214 | -0.104  0.085  0.006 |
## 5                    | -0.700  2.863  0.072 |  1.340 14.132  0.265 |
## 6                    |  1.172  8.019  0.498 |  0.066  0.034  0.002 |
## 7                    | -0.374  0.818  0.132 | -0.335  0.882  0.105 |
## 8                    | -0.524  1.605  0.056 |  0.172  0.232  0.006 |
## 9                    | -0.009  0.000  0.000 | -0.347  0.945  0.071 |
## 10                   | -0.435  1.104  0.093 | -0.482  1.824  0.114 |
##                       Dim.3    ctr   cos2  
## 1                     0.123  0.168  0.012 |
## 2                    -0.812  7.304  0.100 |
## 3                    -0.655  4.755  0.067 |
## 4                    -0.042  0.020  0.001 |
## 5                     0.041  0.018  0.000 |
## 6                    -0.024  0.006  0.000 |
## 7                    -0.012  0.002  0.000 |
## 8                    -0.044  0.021  0.000 |
## 9                     0.062  0.043  0.002 |
## 10                    0.050  0.027  0.001 |
## 
## Categories (the 10 first)
##                         Dim.1    ctr   cos2 v.test    Dim.2    ctr   cos2
## <20                  |  1.796  5.654  0.242  3.188 |  0.405  0.386  0.012
## >100                 | -0.439  0.675  0.031 -1.145 | -0.230  0.250  0.009
## 20-50                | -0.021  0.003  0.000 -0.080 | -0.379  1.246  0.050
## 50-100               | -0.469  2.054  0.130 -2.339 | -0.345  1.494  0.070
## all                  |  1.515  6.704  0.302  3.562 |  0.427  0.716  0.024
## noinfo               | -1.299  1.973  0.082 -1.860 |  3.861 23.447  0.727
## R_BR_type_1          |  1.825  7.780  0.341  3.787 |  0.061  0.012  0.000
## R_BR_type_2          |  2.080  5.055  0.211  2.977 |  0.763  0.916  0.028
## R_BR_type_3          | -0.436  3.437  0.490 -4.538 | -0.051  0.064  0.007
## R_BR_type_12         |  0.882  0.909  0.038  1.263 | -0.203  0.065  0.002
##                      v.test    Dim.3    ctr   cos2 v.test  
## <20                   0.718 |  1.732  9.958  0.225  3.073 |
## >100                 -0.600 |  0.349  0.811  0.020  0.912 |
## 20-50                -1.442 |  0.378  1.742  0.049  1.437 |
## 50-100               -1.719 | -0.283  1.420  0.048 -1.413 |
## all                   1.003 | -1.352 10.119  0.241 -3.179 |
## noinfo                5.526 | -0.081  0.014  0.000 -0.115 |
## R_BR_type_1           0.127 | -0.402  0.717  0.017 -0.835 |
## R_BR_type_2           1.092 |  0.876  1.699  0.037  1.254 |
## R_BR_type_3          -0.535 | -0.101  0.348  0.026 -1.049 |
## R_BR_type_12         -0.290 | -1.255  3.487  0.077 -1.796 |
## 
## Categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## R_BR_sowspersection            | 0.679 0.814 0.506 |
## R_BR_type                      | 0.702 0.040 0.488 |
## R_BR_noise                     | 0.038 0.237 0.115 |
## R_BR_airqual                   | 0.118 0.120 0.053 |
## R_BR_outC                      | 0.397 0.897 0.591 |
## R_BR_floorbetmetplastwoodother | 0.162 0.074 0.038 |
## R_BR_floorsolid_0981_2         | 0.718 0.029 0.005 |
## R_BR_kuivaliete                | 0.613 0.013 0.270 |
## R_BR_PREGsame                  | 0.428 0.673 0.035 |
## OUT_SOW_mort_dic               | 0.127 0.061 0.000 |
## 
## Supplementary categories
##                         Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## OUT_SOW_totrem_dic_0 |  0.234  0.052  1.485 | -0.118  0.013 -0.748 |
## OUT_SOW_totrem_dic_1 | -0.224  0.052 -1.485 |  0.113  0.013  0.748 |
## OUT_SOW_cull_dic_0   | -0.009  0.000 -0.062 |  0.122  0.016  0.808 |
## OUT_SOW_cull_dic_1   |  0.010  0.000  0.062 | -0.128  0.016 -0.808 |
##                       Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0  0.176  0.029  1.112 |
## OUT_SOW_totrem_dic_1 -0.168  0.029 -1.112 |
## OUT_SOW_cull_dic_0    0.059  0.004  0.394 |
## OUT_SOW_cull_dic_1   -0.062  0.004 -0.394 |
## 
## Supplementary categorical variables (eta2)
##                        Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic   | 0.052 0.013 0.029 |
## OUT_SOW_cull_dic     | 0.000 0.016 0.004 |
## 
## Supplementary continuous variables
##                         Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM  | -0.361 |  0.204 | -0.120 |
## OUT_SOW_cullproNUM   | -0.024 |  0.008 | -0.016 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("35", "6", "42", "27", "34", "39", "18", "5", "3", "12")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by5* and *5*. :
## 
## - high frequency for the factors *R_BR_sowspersection=noinfo*, *R_BR_PREGsame=R_BR_PREGsame_Not Assigned*, *R_BR_outC=R_BR_outC_24* and *R_BR_outC=R_BR_outC_20* (factors are sorted from the most common).
## 
## The cluster 2 is made of individuals such as*. This group is characterized by12* and *12*. :
## 
## - high frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_sowspersection=50-100*, *R_BR_type=R_BR_type_3*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_BR_floorbetmetplastwoodother=betmet* and *R_BR_noise=R_BR_noise_1* (factors are sorted from the most common).
## - low frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_BR_sowspersection=all*, *R_BR_type=R_BR_type_1*, *R_BR_floorbetmetplastwoodother=bet*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_BR_sowspersection=<20* and *R_BR_noise=R_BR_noise_0* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by3* and *3*. :
## 
## - high frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_BR_sowspersection=all*, *R_BR_type=R_BR_type_1*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_BR_sowspersection=<20*, *R_BR_floorbetmetplastwoodother=bet* and *R_BR_type=R_BR_type_2* (factors are sorted from the most common).
## - low frequency for the factors *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_type=R_BR_type_3*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_BR_sowspersection=50-100* and *R_BR_floorbetmetplastwoodother=betmet* (factors are sorted from the rarest).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Housing; gestation unit

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roompr.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 23
## $ R_PR_sectionsNUM_NO    <int> 6, 1, 1, 5, NA, 1, 1, 2, 1, 3, 1, 1, 6,...
## $ R_PR_sowsinsecNUM_NO   <fctr> 48, 27, 200, 60, 365, 32, 120, 60, 66,...
## $ R_PR_sowsNUM_NO        <fctr> 8, 27, 12, 20, 12, 6, 20, 7, 16, 36, 8...
## $ R_PR_areapersow_NUM_NO <fctr> 3,0, 3,1, 4,1, 2,6, 3,8, 4,4, 2,3, 5,6...
## $ R_PR_areaNUM           <fctr> 3,0, 2,1, 4,1, 2,6, 3,8, , 2,3, 2,3, 3...
## $ R_PR_areapersow        <int> 2, 2, 4, 1, 4, 4, 1, 4, 3, 3, 3, 2, 1, ...
## $ R_PR_crareaNUM_NO      <fctr> , , 1,5, 1,9, 2,0, 0,0, 1,9, 1,6, 1,4,...
## $ R_PR_nonoise           <int> 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, ...
## $ R_PR_air               <int> 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, ...
## $ R_PR_CNUM_NO           <int> 17, 19, 18, 25, 18, 23, 19, 12, 28, 25,...
## $ R_PR_floorsolidNUM_NO  <int> 70, 80, 99, 40, 70, 100, 80, 100, 67, 1...
## $ R_PR_floorsolid_0791_2 <int> 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, ...
## $ R_PR_bedmatyn          <int> 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, ...
## $ R_PR_bedmatamount      <int> 0, 2, 1, 0, 0, 1, 0, 1, 3, 1, 2, 0, 0, ...
## $ R_PR_floornoslip       <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...
## $ R_PR_dirtNUM_NO        <int> 10, 0, 0, 20, NA, 0, 0, 0, 20, 20, 0, 1...
## $ R_PR_dirtmed           <int> 1, 1, 1, 2, NA, 1, 1, 1, 2, 2, 1, 2, 2,...
## $ OUT_SOW_mort_proNUM    <int> 5, 5, 8, 27, 10, 0, 17, 13, 0, 6, 9, 5,...
## $ OUT_SOW_mort_dic       <int> 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...
## $ OUT_SOW_totremproNUM   <int> 34, 38, 53, 57, 65, 64, 47, 44, 24, 34,...
## $ OUT_SOW_totrem_dic     <int> 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, ...
## $ OUT_SOW_cullproNUM     <int> 29, 33, 45, 30, 55, 64, 30, 31, 24, 28,...
## $ OUT_SOW_cull_dic       <int> 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, ...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
## [23] FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "R_PR_dirtmed"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="green") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
R_PR_sectionsNUM_NO (mean (sd)) 1.83 (1.44) 2.16 (1.50) 0.469
R_PR_sowsinsecNUM_NO (mean (sd)) 17.74 (10.80) 19.00 (8.99) 0.682
R_PR_sowsNUM_NO (mean (sd)) 9.61 (6.14) 11.20 (5.62) 0.383
R_PR_areapersow_NUM_NO (mean (sd)) 11.91 (6.12) 10.75 (6.95) 0.563
R_PR_crareaNUM_NO (mean (sd)) 6.39 (4.31) 6.80 (5.26) 0.781
R_PR_CNUM_NO (mean (sd)) 6.43 (2.97) 7.10 (3.70) 0.517
R_PR_floorsolidNUM_NO (mean (sd)) 10.61 (4.39) 8.35 (4.16) 0.092
R_PR_dirtNUM_NO (mean (sd)) 2.76 (2.30) 3.11 (2.11) 0.627
R_PR_areaNUM (mean (sd)) 10.48 (7.88) 12.40 (6.16) 0.383
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
R_PR_areapersow (%) 0.593
1 4 (17.4) 7 ( 35.0)
2 8 (34.8) 5 ( 25.0)
3 5 (21.7) 3 ( 15.0)
4 6 (26.1) 5 ( 25.0)
R_PR_nonoise = 1 (%) 12 (52.2) 12 ( 60.0) 0.836
R_PR_air = 1 (%) 2 ( 8.7) 9 ( 45.0) 0.018
R_PR_floorsolid_0791_2 = 2 (%) 12 (52.2) 5 ( 25.0) 0.132
R_PR_bedmatyn = 1 (%) 18 (78.3) 12 ( 60.0) 0.333
R_PR_bedmatamount (%) 0.387
0 5 (21.7) 8 ( 40.0)
1 9 (39.1) 4 ( 20.0)
2 5 (21.7) 3 ( 15.0)
3 4 (17.4) 5 ( 25.0)
R_PR_floornoslip = 1 (%) 3 (13.0) 2 ( 10.0) 1.000
R_PR_dirtmed = 2 (%) 9 (42.9) 9 ( 50.0) 0.901
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
R_PR_sectionsNUM_NO (mean (sd)) 2.00 (1.51) 1.95 (1.43) 0.913
R_PR_sowsinsecNUM_NO (mean (sd)) 18.59 (10.13) 18.05 (9.90) 0.860
R_PR_sowsNUM_NO (mean (sd)) 11.32 (5.75) 9.33 (5.99) 0.274
R_PR_areapersow_NUM_NO (mean (sd)) 11.59 (6.02) 11.14 (7.04) 0.823
R_PR_crareaNUM_NO (mean (sd)) 6.68 (4.52) 6.48 (5.04) 0.889
R_PR_CNUM_NO (mean (sd)) 7.32 (3.98) 6.14 (2.35) 0.248
R_PR_floorsolidNUM_NO (mean (sd)) 9.73 (4.48) 9.38 (4.38) 0.799
R_PR_dirtNUM_NO (mean (sd)) 2.75 (2.31) 3.11 (2.11) 0.620
R_PR_areaNUM (mean (sd)) 12.45 (6.72) 10.24 (7.50) 0.313
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
R_PR_areapersow (%) 0.108
1 6 (27.3) 5 ( 23.8)
2 5 (22.7) 8 ( 38.1)
3 7 (31.8) 1 ( 4.8)
4 4 (18.2) 7 ( 33.3)
R_PR_nonoise = 1 (%) 11 (50.0) 13 ( 61.9) 0.632
R_PR_air = 1 (%) 5 (22.7) 6 ( 28.6) 0.929
R_PR_floorsolid_0791_2 = 2 (%) 10 (45.5) 7 ( 33.3) 0.617
R_PR_bedmatyn = 1 (%) 16 (72.7) 14 ( 66.7) 0.920
R_PR_bedmatamount (%) 0.863
0 6 (27.3) 7 ( 33.3)
1 6 (27.3) 7 ( 33.3)
2 5 (22.7) 3 ( 14.3)
3 5 (22.7) 4 ( 19.0)
R_PR_floornoslip = 1 (%) 1 ( 4.5) 4 ( 19.0) 0.314
R_PR_dirtmed = 2 (%) 8 (40.0) 10 ( 52.6) 0.639
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(12,13),quali.sup=c(10:11), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(12, 13), quali.sup = c(10:11),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.351   0.239   0.162   0.152   0.132   0.129
## % of var.             22.585  15.351  10.405   9.759   8.513   8.268
## Cumulative % of var.  22.585  37.936  48.341  58.100  66.613  74.881
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.094   0.074   0.064   0.053   0.048   0.038
## % of var.              6.069   4.768   4.097   3.422   3.087   2.468
## Cumulative % of var.  80.950  85.718  89.815  93.237  96.324  98.792
##                       Dim.13  Dim.14
## Variance               0.019   0.000
## % of var.              1.208   0.000
## Cumulative % of var. 100.000 100.000
## 
## Individuals (the 10 first)
##                             Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                        |  0.500  1.653  0.120 |  0.086  0.073  0.004 |
## 2                        | -0.407  1.095  0.126 | -0.468  2.134  0.167 |
## 3                        | -0.931  5.744  0.721 |  0.405  1.599  0.136 |
## 4                        |  1.054  7.348  0.687 |  0.304  0.898  0.057 |
## 5                        |  0.546  1.971  0.115 |  1.141 12.678  0.501 |
## 6                        | -0.931  5.744  0.721 |  0.405  1.599  0.136 |
## 7                        |  0.586  2.274  0.205 |  0.570  3.168  0.194 |
## 8                        | -0.781  4.040  0.494 |  0.507  2.502  0.208 |
## 9                        | -0.058  0.022  0.002 | -0.702  4.804  0.348 |
## 10                       | -0.544  1.962  0.219 | -0.352  1.206  0.092 |
##                           Dim.3    ctr   cos2  
## 1                         0.359  1.848  0.062 |
## 2                         0.548  4.308  0.228 |
## 3                         0.050  0.036  0.002 |
## 4                        -0.001  0.000  0.000 |
## 5                        -0.071  0.073  0.002 |
## 6                         0.050  0.036  0.002 |
## 7                         0.110  0.174  0.007 |
## 8                        -0.093  0.124  0.007 |
## 9                        -0.483  3.353  0.165 |
## 10                        0.229  0.755  0.039 |
## 
## Categories (the 10 first)
##                             Dim.1    ctr   cos2 v.test    Dim.2    ctr
## R_PR_areapersow_1        |  0.841  5.719  0.243  3.195 | -0.190  0.431
## R_PR_areapersow_2        |  0.410  1.611  0.073  1.751 | -0.318  1.424
## R_PR_areapersow_3        | -0.497  1.452  0.056 -1.539 | -0.515  2.292
## R_PR_areapersow_4        | -0.965  7.527  0.320 -3.665 |  0.941 10.529
## R_PR_nonoise_0           | -0.280  1.093  0.062 -1.612 |  0.466  4.473
## R_PR_nonoise_1           |  0.221  0.865  0.062  1.612 | -0.369  3.541
## R_PR_air_0               | -0.248  1.443  0.178 -2.737 | -0.266  2.459
## R_PR_air_1               |  0.720  4.197  0.178  2.737 |  0.775  7.152
## R_PR_floorsolid_0791_2_1 |  0.553  5.839  0.467  4.429 | -0.053  0.079
## R_PR_floorsolid_0791_2_2 | -0.845  8.931  0.467 -4.429 |  0.081  0.121
##                            cos2 v.test    Dim.3    ctr   cos2 v.test  
## R_PR_areapersow_1         0.012 -0.723 | -0.663  7.726  0.151 -2.520 |
## R_PR_areapersow_2         0.044 -1.357 |  0.787 12.863  0.269  3.359 |
## R_PR_areapersow_3         0.061 -1.594 | -0.157  0.315  0.006 -0.487 |
## R_PR_areapersow_4         0.304  3.574 | -0.153  0.411  0.008 -0.581 |
## R_PR_nonoise_0            0.172  2.690 | -0.118  0.421  0.011 -0.680 |
## R_PR_nonoise_1            0.172 -2.690 |  0.093  0.334  0.011  0.680 |
## R_PR_air_0                0.207 -2.945 | -0.205  2.141  0.122 -2.263 |
## R_PR_air_1                0.207  2.945 |  0.595  6.227  0.122  2.263 |
## R_PR_floorsolid_0791_2_1  0.004 -0.425 | -0.334  4.629  0.171 -2.677 |
## R_PR_floorsolid_0791_2_2  0.004  0.425 |  0.511  7.080  0.171  2.677 |
## 
## Categorical variables (eta2)
##                            Dim.1 Dim.2 Dim.3  
## R_PR_areapersow          | 0.516 0.315 0.310 |
## R_PR_nonoise             | 0.062 0.172 0.011 |
## R_PR_air                 | 0.178 0.207 0.122 |
## R_PR_floorsolid_0791_2   | 0.467 0.004 0.171 |
## R_PR_bedmatyn            | 0.615 0.231 0.018 |
## R_PR_bedmatamount        | 0.818 0.663 0.642 |
## R_PR_floornoslip         | 0.073 0.151 0.064 |
## R_PR_dirtmed             | 0.274 0.356 0.052 |
## OUT_SOW_mort_dic         | 0.160 0.050 0.067 |
## 
## Supplementary categories
##                             Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## OUT_SOW_totrem_dic_0     | -0.511  0.249 -3.237 | -0.081  0.006 -0.512 |
## OUT_SOW_totrem_dic_1     |  0.488  0.249  3.237 |  0.077  0.006  0.512 |
## OUT_SOW_cull_dic_0       | -0.107  0.012 -0.712 | -0.040  0.002 -0.266 |
## OUT_SOW_cull_dic_1       |  0.113  0.012  0.712 |  0.042  0.002  0.266 |
##                           Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0      0.052  0.003  0.327 |
## OUT_SOW_totrem_dic_1     -0.049  0.003 -0.327 |
## OUT_SOW_cull_dic_0       -0.103  0.011 -0.685 |
## OUT_SOW_cull_dic_1        0.108  0.011  0.685 |
## 
## Supplementary categorical variables (eta2)
##                            Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic       | 0.249 0.006 0.003 |
## OUT_SOW_cull_dic         | 0.012 0.002 0.011 |
## 
## Supplementary continuous variables
##                             Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM      |  0.469 |  0.144 | -0.059 |
## OUT_SOW_cullproNUM       |  0.175 |  0.103 |  0.046 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("6", "20", "3", "4", "8", "40", "24", "27", "39", "5", "13", 
## "19")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by3* and *3*. :
## 
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_areapersow=R_PR_areapersow_4*, *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_dirtmed=R_PR_dirtmed_1* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_areapersow=R_PR_areapersow_1*, *R_PR_dirtmed=R_PR_dirtmed_2*, *R_PR_bedmatamount=R_PR_bedmatamount_3* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the rarest).
## 
## The cluster 2 is made of individuals sharing :
## 
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_2* and *R_PR_dirtmed=R_PR_dirtmed_2* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_air=R_PR_air_1* and *R_PR_areapersow=R_PR_areapersow_4* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by4* and *4*. :
## 
## - high frequency for the factors *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_air=R_PR_air_1* and *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_bedmatamount=R_PR_bedmatamount_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0* and *R_PR_bedmatamount=R_PR_bedmatamount_2* (factors are sorted from the rarest).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Housing; farrowing unit

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="roomfar.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 18
## $ R_FAR_secNUM_NO                      <int> 6, 1, 4, 4, 12, 2, 4, 2, ...
## $ R_FAR_pensinsecNUM_NO                <int> 28, 10, 15, 40, 24, 18, 1...
## $ R_FARpenNUM_NO                       <fctr> 4,8, 4,6, 5,6, 3,9, , 8,...
## $ R_FAR_pensize_med4.9                 <fctr> 1,0, 1,0, 2,0, 1,0, , 2,...
## $ R_FAR_noise                          <int> 1, 1, 0, 1, 0, 1, 0, 1, 0...
## $ R_FAR_airqual                        <int> 1, 0, 0, 0, 0, 0, 1, 0, 0...
## $ R_FAR_C_NUM_NO                       <int> 19, 18, 24, 26, 23, 26, 2...
## $ R_FAR_floorsolidNUM_NO               <int> 0, 100, 50, 60, 10, 100, ...
## $ R_FAR_floorsolid_all0_100_100_2_muu1 <int> 0, 2, 1, 1, 0, 2, 0, 1, 1...
## $ R_FAR_kuivaliete                     <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_FAR_kunto                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_FAR_nonslippery                    <int> 0, 0, 0, 0, 0, 0, 1, 0, 0...
## $ OUT_SOW_mort_proNUM                  <int> 5, 5, 8, 27, 10, 0, 17, 1...
## $ OUT_SOW_mort_dic                     <int> 0, 0, 0, 1, 1, 0, 1, 1, 0...
## $ OUT_SOW_totremproNUM                 <int> 34, 38, 53, 57, 65, 64, 4...
## $ OUT_SOW_totrem_dic                   <int> 0, 0, 1, 1, 1, 1, 1, 0, 0...
## $ OUT_SOW_cullproNUM                   <int> 29, 33, 45, 30, 55, 64, 3...
## $ OUT_SOW_cull_dic                     <int> 0, 0, 1, 0, 1, 1, 0, 0, 0...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("M_paras")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## character(0)
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="darkgreen") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
R_FAR_secNUM_NO (mean (sd)) 3.22 (2.17) 4.15 (2.80) 0.226
R_FAR_pensinsecNUM_NO (mean (sd)) 8.43 (4.90) 12.10 (5.16) 0.022
R_FARpenNUM_NO (mean (sd)) 12.26 (5.99) 8.65 (4.57) 0.034
R_FAR_C_NUM_NO (mean (sd)) 5.09 (2.27) 5.55 (2.80) 0.553
R_FAR_floorsolidNUM_NO (mean (sd)) 6.04 (3.65) 6.75 (3.21) 0.507
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
R_FAR_pensize_med4.9 (%) 0.340
0 ( 0.0) 1 ( 5.0)
1,0 11 (47.8) 12 ( 60.0)
2,0 12 (52.2) 7 ( 35.0)
R_FAR_noise = 1 (%) 11 (47.8) 8 ( 40.0) 0.836
R_FAR_airqual = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
R_FAR_floorsolid_all0_100_100_2_muu1 (%) 0.132
0 6 (26.1) 5 ( 25.0)
1 13 (56.5) 15 ( 75.0)
2 4 (17.4) 0 ( 0.0)
R_FAR_kuivaliete = 2 (%) 17 (73.9) 19 ( 95.0) 0.146
R_FAR_kunto = 1 (%) 1 ( 4.3) 3 ( 15.0) 0.501
R_FAR_nonslippery = 1 (%) 5 (21.7) 2 ( 10.0) 0.531
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
R_FAR_secNUM_NO (mean (sd)) 3.00 (2.23) 4.33 (2.63) 0.080
R_FAR_pensinsecNUM_NO (mean (sd)) 9.36 (4.30) 10.95 (6.17) 0.331
R_FARpenNUM_NO (mean (sd)) 10.45 (5.03) 10.71 (6.29) 0.882
R_FAR_C_NUM_NO (mean (sd)) 5.23 (2.86) 5.38 (2.16) 0.844
R_FAR_floorsolidNUM_NO (mean (sd)) 6.82 (3.54) 5.90 (3.33) 0.389
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
R_FAR_pensize_med4.9 (%) 0.491
0 ( 0.0) 1 ( 4.8)
1,0 13 (59.1) 10 ( 47.6)
2,0 9 (40.9) 10 ( 47.6)
R_FAR_noise = 1 (%) 12 (54.5) 7 ( 33.3) 0.274
R_FAR_airqual = 1 (%) 2 ( 9.1) 2 ( 9.5) 1.000
R_FAR_floorsolid_all0_100_100_2_muu1 (%) 0.379
0 4 (18.2) 7 ( 33.3)
1 15 (68.2) 13 ( 61.9)
2 3 (13.6) 1 ( 4.8)
R_FAR_kuivaliete = 2 (%) 19 (86.4) 17 ( 81.0) 0.946
R_FAR_kunto = 1 (%) 1 ( 4.5) 3 ( 14.3) 0.566
R_FAR_nonslippery = 1 (%) 4 (18.2) 3 ( 14.3) 1.000
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(11,12),quali.sup=c(9:10), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(11, 12), quali.sup = c(9:10),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.269   0.214   0.167   0.154   0.115   0.106
## % of var.             21.558  17.115  13.324  12.314   9.233   8.504
## Cumulative % of var.  21.558  38.673  51.997  64.311  73.544  82.048
##                        Dim.7   Dim.8   Dim.9  Dim.10
## Variance               0.081   0.072   0.047   0.024
## % of var.              6.452   5.787   3.765   1.947
## Cumulative % of var.  88.500  94.288  98.053 100.000
## 
## Individuals (the 10 first)
##                                           Dim.1    ctr   cos2    Dim.2
## 1                                      | -0.493  2.099  0.120 |  1.008
## 2                                      |  1.095 10.346  0.524 |  0.549
## 3                                      |  0.756  4.939  0.509 | -0.009
## 4                                      | -0.275  0.654  0.137 | -0.336
## 5                                      | -0.577  2.878  0.056 |  0.341
## 6                                      |  1.358 15.911  0.789 |  0.364
## 7                                      | -0.877  6.638  0.294 |  1.058
## 8                                      | -0.012  0.001  0.000 | -0.521
## 9                                      | -0.023  0.004  0.001 | -0.113
## 10                                     |  0.223  0.428  0.088 | -0.348
##                                           ctr   cos2    Dim.3    ctr
## 1                                      11.034  0.503 | -0.185  0.476
## 2                                       3.272  0.132 | -0.087  0.106
## 3                                       0.001  0.000 |  0.143  0.285
## 4                                       1.230  0.205 | -0.083  0.096
## 5                                       1.262  0.020 |  1.482 30.684
## 6                                       1.440  0.057 |  0.012  0.002
## 7                                      12.173  0.428 | -0.127  0.224
## 8                                       2.951  0.452 |  0.017  0.004
## 9                                       0.139  0.028 | -0.124  0.216
## 10                                      1.317  0.214 | -0.287  1.149
##                                          cos2  
## 1                                       0.017 |
## 2                                       0.003 |
## 3                                       0.018 |
## 4                                       0.012 |
## 5                                       0.371 |
## 6                                       0.000 |
## 7                                       0.006 |
## 8                                       0.000 |
## 9                                       0.034 |
## 10                                      0.145 |
## 
## Categories (the 10 first)
##                                           Dim.1    ctr   cos2 v.test  
## V1                                     | -1.112  1.335  0.029 -1.112 |
## 1,0                                    | -0.467  5.422  0.251 -3.249 |
## 2,0                                    |  0.624  7.992  0.309  3.601 |
## R_FAR_noise_0                          |  0.032  0.027  0.001  0.235 |
## R_FAR_noise_1                          | -0.041  0.034  0.001 -0.235 |
## R_FAR_airqual_0                        |  0.111  0.523  0.121  2.255 |
## R_FAR_airqual_1                        | -1.087  5.095  0.121 -2.255 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0 | -0.748  6.632  0.192 -2.841 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 | -0.064  0.125  0.008 -0.569 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2 |  2.506 27.097  0.644  5.201 |
##                                         Dim.2    ctr   cos2 v.test  
## V1                                      0.737  0.737  0.013  0.737 |
## 1,0                                     0.292  2.658  0.098  2.027 |
## 2,0                                    -0.392  3.963  0.122 -2.259 |
## R_FAR_noise_0                           0.082  0.219  0.008  0.597 |
## R_FAR_noise_1                          -0.104  0.277  0.008 -0.597 |
## R_FAR_airqual_0                        -0.229  2.770  0.510 -4.626 |
## R_FAR_airqual_1                         2.229 27.007  0.510  4.626 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0  1.249 23.334  0.537  4.748 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 -0.625 14.875  0.730 -5.536 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2  0.941  4.812  0.091  1.953 |
##                                         Dim.3    ctr   cos2 v.test  
## V1                                      3.632 23.030  0.314  3.632 |
## 1,0                                    -0.234  2.191  0.063 -1.624 |
## 2,0                                     0.092  0.278  0.007  0.528 |
## R_FAR_noise_0                           0.378  5.993  0.181  2.755 |
## R_FAR_noise_1                          -0.478  7.570  0.181 -2.755 |
## R_FAR_airqual_0                        -0.025  0.043  0.006 -0.510 |
## R_FAR_airqual_1                         0.246  0.422  0.006  0.510 |
## R_FAR_floorsolid_all0_100_100_2_muu1_0  0.249  1.189  0.021  0.945 |
## R_FAR_floorsolid_all0_100_100_2_muu1_1 -0.139  0.948  0.036 -1.233 |
## R_FAR_floorsolid_all0_100_100_2_muu1_2  0.290  0.589  0.009  0.603 |
## 
## Categorical variables (eta2)
##                                          Dim.1 Dim.2 Dim.3  
## R_FAR_pensize_med4.9                   | 0.318 0.126 0.340 |
## R_FAR_noise                            | 0.001 0.008 0.181 |
## R_FAR_airqual                          | 0.121 0.510 0.006 |
## R_FAR_floorsolid_all0_100_100_2_muu1   | 0.730 0.736 0.036 |
## R_FAR_kuivaliete                       | 0.626 0.156 0.041 |
## R_FAR_kunto                            | 0.057 0.017 0.109 |
## R_FAR_nonslippery                      | 0.065 0.056 0.375 |
## OUT_SOW_mort_dic                       | 0.237 0.102 0.245 |
## 
## Supplementary categories
##                                           Dim.1   cos2 v.test    Dim.2
## OUT_SOW_totrem_dic_0                   |  0.290  0.080  1.835 | -0.014
## OUT_SOW_totrem_dic_1                   | -0.277  0.080 -1.835 |  0.014
## OUT_SOW_cull_dic_0                     |  0.054  0.003  0.356 | -0.041
## OUT_SOW_cull_dic_1                     | -0.056  0.003 -0.356 |  0.043
##                                          cos2 v.test    Dim.3   cos2
## OUT_SOW_totrem_dic_0                    0.000 -0.090 | -0.343  0.112
## OUT_SOW_totrem_dic_1                    0.000  0.090 |  0.328  0.112
## OUT_SOW_cull_dic_0                      0.002 -0.275 | -0.233  0.057
## OUT_SOW_cull_dic_1                      0.002  0.275 |  0.244  0.057
##                                        v.test  
## OUT_SOW_totrem_dic_0                   -2.172 |
## OUT_SOW_totrem_dic_1                    2.172 |
## OUT_SOW_cull_dic_0                     -1.546 |
## OUT_SOW_cull_dic_1                      1.546 |
## 
## Supplementary categorical variables (eta2)
##                                          Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic                     | 0.080 0.000 0.112 |
## OUT_SOW_cull_dic                       | 0.003 0.002 0.057 |
## 
## Supplementary continuous variables
##                                           Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM                    | -0.461 | -0.243 |  0.359 |
## OUT_SOW_cullproNUM                     | -0.129 | -0.033 |  0.253 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("14", "18", "6", "7", "2", "1", "43", "20", "3", "8")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by1* and *1*. :
## 
## - high frequency for the factors *R_FAR_airqual=R_FAR_airqual_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_airqual=R_FAR_airqual_0* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* (factors are sorted from the rarest).
## 
## The 1st cluster is made of individuals such as *8*. This group is characterized by :
## 
## - high frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* and *R_FAR_airqual=R_FAR_airqual_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_FAR_airqual=R_FAR_airqual_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by2* and *2*. :
## 
## - high frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2* and *OUT_SOW_mort_dic=OUT_SOW_mort_dic_0* (factors are sorted from the most common).
## - low frequency for the factors *R_FAR_kuivaliete=R_FAR_kuivaliete_2* and *OUT_SOW_mort_dic=OUT_SOW_mort_dic_1* (factors are sorted from the rarest).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"

Housing

# load data
setwd("~/GitHub/tilataso")
library(readr)
library(FactoMineR)
library(FactoInvestigate)
library(factoextra)
library(dplyr)
library(explor)
med<-read.csv(file="room.csv", header=TRUE)
glimpse(med)
## Observations: 43
## Variables: 48
## $ R_BR_sowspersection                  <fctr> >100, all, all, 50-100, ...
## $ R_BR_area2_NUM                       <fctr> 1,5275, group, 1,536, 1,...
## $ R_BR_type                            <int> 3, 1, 3, 3, 3, 1, 3, 3, 3...
## $ R_BR_noise                           <int> 0, 1, 0, 0, 0, 0, 1, 0, 1...
## $ R_BR_pest_NO                         <int> 0, 1, 1, 0, 0, 0, 1, 1, 0...
## $ R_BR_airqual                         <int> 1, 0, 0, 1, 1, 0, 0, 1, 0...
## $ R_BR_C_NUM_NO                        <fctr> 19, 0, 18, 26, 19, 23, 1...
## $ R_BR_outC                            <fctr> , 8, 15, 25, 24, , , 2, ...
## $ R_BR_floorbetmetplastwoodother       <fctr> bet, bet, bet, betmet, b...
## $ R_BR_floorsolid_NUM_NO               <int> 80, 80, 99, 80, 70, 100, ...
## $ R_BR_floorsolid_0981_2               <int> 1, 1, 2, 1, 1, 2, 1, 1, 1...
## $ R_BR_kuivaliete                      <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_BR_PREGsame                        <int> 0, 0, 1, 0, 0, 1, 0, 0, 1...
## $ R_PR_sectionsNUM_NO                  <int> 6, 1, 1, 5, NA, 1, 1, 2, ...
## $ R_PR_sowsinsecNUM_NO                 <fctr> 48, 27, 200, 60, 365, 32...
## $ R_PR_sowsNUM_NO                      <fctr> 8, 27, 12, 20, 12, 6, 20...
## $ R_PR_areapersow_NUM_NO               <fctr> 3,0, 3,1, 4,1, 2,6, 3,8,...
## $ R_PR_areaNUM                         <fctr> 3,0, 2,1, 4,1, 2,6, 3,8,...
## $ R_PR_areapersow                      <int> 2, 2, 4, 1, 4, 4, 1, 4, 3...
## $ R_PR_crareaNUM_NO                    <fctr> , , 1,5, 1,9, 2,0, 0,0, ...
## $ R_PR_nonoise                         <int> 0, 1, 0, 1, 0, 0, 1, 0, 1...
## $ R_PR_air                             <int> 0, 0, 0, 1, 1, 0, 1, 0, 0...
## $ R_PR_CNUM_NO                         <int> 17, 19, 18, 25, 18, 23, 1...
## $ R_PR_floorsolidNUM_NO                <int> 70, 80, 99, 40, 70, 100, ...
## $ R_PR_floorsolid_0791_2               <int> 1, 2, 2, 1, 1, 2, 2, 2, 1...
## $ R_PR_bedmatyn                        <int> 0, 1, 1, 0, 0, 1, 0, 1, 1...
## $ R_PR_bedmatamount                    <int> 0, 2, 1, 0, 0, 1, 0, 1, 3...
## $ R_PR_floornoslip                     <int> 1, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_PR_dirtNUM_NO                      <int> 10, 0, 0, 20, NA, 0, 0, 0...
## $ R_PR_dirtmed                         <int> 1, 1, 1, 2, NA, 1, 1, 1, ...
## $ R_FAR_secNUM_NO                      <int> 6, 1, 4, 4, 12, 2, 4, 2, ...
## $ R_FAR_pensinsecNUM_NO                <int> 28, 10, 15, 40, 24, 18, 1...
## $ R_FARpenNUM_NO                       <fctr> 4,8, 4,6, 5,6, 3,9, , 8,...
## $ R_FAR_pensize_med4.9                 <fctr> 1,0, 1,0, 2,0, 1,0, , 2,...
## $ R_FAR_noise                          <int> 1, 1, 0, 1, 0, 1, 0, 1, 0...
## $ R_FAR_airqual                        <int> 1, 0, 0, 0, 0, 0, 1, 0, 0...
## $ R_FAR_C_NUM_NO                       <int> 19, 18, 24, 26, 23, 26, 2...
## $ R_FAR_floorsolidNUM_NO               <int> 0, 100, 50, 60, 10, 100, ...
## $ R_FAR_floorsolid_all0_100_100_2_muu1 <int> 0, 2, 1, 1, 0, 2, 0, 1, 1...
## $ R_FAR_kuivaliete                     <int> 2, 1, 1, 2, 2, 1, 2, 2, 2...
## $ R_FAR_kunto                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ R_FAR_nonslippery                    <int> 0, 0, 0, 0, 0, 0, 1, 0, 0...
## $ OUT_SOW_mort_proNUM                  <int> 5, 5, 8, 27, 10, 0, 17, 1...
## $ OUT_SOW_mort_dic                     <int> 0, 0, 0, 1, 1, 0, 1, 1, 0...
## $ OUT_SOW_totremproNUM                 <int> 34, 38, 53, 57, 65, 64, 4...
## $ OUT_SOW_totrem_dic                   <int> 0, 0, 1, 1, 1, 1, 1, 0, 0...
## $ OUT_SOW_cullproNUM                   <int> 29, 33, 45, 30, 55, 64, 3...
## $ OUT_SOW_cull_dic                     <int> 0, 0, 1, 0, 1, 1, 0, 0, 0...
med<-med%>%mutate_all(as.factor)
med$OUT_SOW_cullproNUM 
##  [1] 29  33  45  30  55  64  30  31  24  28  29  36  70  27  42  22  40 
## [18] 29  38  38  27  30  44  45  34  45  41  35  49  32  39  80  14  24 
## [35] 35  56  39  50  42  35  33  107 42 
## 28 Levels: 14 22 24 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 ... 107
colnames(med)=="OUT_SOW_cullproNUM"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE  TRUE FALSE
colnames(med)=="OUT_SOWmortpro"
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE
med$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
med$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medcat<-med %>% select(-ends_with("NUM_NO"),-ends_with("dosage_NO"),-ends_with("NUM"))
medcat<-medcat%>%mutate_all(as.factor)
mednum<-med %>% select(ends_with("NUM_NO"),(ends_with("dosage_NO")),(ends_with("NUM")))
mednum<-mednum%>%mutate_all(as.numeric)
medmca<-medcat
medmca$OUT_SOW_mort_proNUM<-as.numeric(med$OUT_SOW_mort_proNUM)
medmca$OUT_SOW_cullproNUM<-as.numeric(med$OUT_SOW_cullproNUM)
medmca<-medmca%>% select(-ends_with("NO"),(-starts_with("R_BR_outC")))


X<-medmca   
colnames(X)[ apply(X, 2, anyNA) ]
## [1] "R_BR_PREGsame" "R_PR_dirtmed"
for (i in 1:ncol(X)) {
  levels(X[,i]) <- c(levels(X[,i]), "Not Assigned")
  X[which(is.na(X[,i])==TRUE),i] <- "Not Assigned"
}

X$OUT_SOW_cullproNUM<-as.numeric(X$OUT_SOW_cullproNUM)
X$OUT_SOW_mort_proNUM<-as.numeric(X$OUT_SOW_mort_proNUM)

medmca<-X 
#colnames(medcat[,1:30])
library(tidyr)
gather(medcat) %>% ggplot(aes(value)) + facet_wrap("key", scales = "free") + geom_bar(fill="orange") + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+   scale_fill_manual("key")

library(dplyr)
library(ggplot2)
#out<-med %>% dplyr::select(ends_with("pro"))
#Matrix of plots
ggpairs(mednum, lower = list(combo = wrap("facethist", bins = 20)), title="Graphical overview of the numeric variables")

library(tableone)
KreateTableOne = function(x, ...){
  t1 = tableone::CreateTableOne(data=x, ...)
  t2 = print(t1, quote=TRUE)
  rownames(t2) = gsub(pattern='\\"', replacement='', rownames(t2))
  colnames(t2) = gsub(pattern='\\"', replacement='', colnames(t2))
  return(t2)
}

Yhteenveto kuolleisuuden mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


meddescap<-cbind(mednum,medcat)
meddesc<-meddescap%>%select(-ends_with("OUT"))
table1 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_mort_dic')
table1%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow mortality") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow mortality
0 1 p test
n 23 20
R_BR_C_NUM_NO (mean (sd)) 13.52 (7.56) 15.80 (8.97) 0.371
R_BR_floorsolid_NUM_NO (mean (sd)) 7.87 (3.08) 7.90 (2.02) 0.970
R_PR_sectionsNUM_NO (mean (sd)) 1.83 (1.44) 2.16 (1.50) 0.469
R_PR_sowsinsecNUM_NO (mean (sd)) 17.74 (10.80) 19.00 (8.99) 0.682
R_PR_sowsNUM_NO (mean (sd)) 9.61 (6.14) 11.20 (5.62) 0.383
R_PR_areapersow_NUM_NO (mean (sd)) 11.91 (6.12) 10.75 (6.95) 0.563
R_PR_crareaNUM_NO (mean (sd)) 6.39 (4.31) 6.80 (5.26) 0.781
R_PR_CNUM_NO (mean (sd)) 6.43 (2.97) 7.10 (3.70) 0.517
R_PR_floorsolidNUM_NO (mean (sd)) 10.61 (4.39) 8.35 (4.16) 0.092
R_PR_dirtNUM_NO (mean (sd)) 2.76 (2.30) 3.11 (2.11) 0.627
R_FAR_secNUM_NO (mean (sd)) 3.22 (2.17) 4.15 (2.80) 0.226
R_FAR_pensinsecNUM_NO (mean (sd)) 8.43 (4.90) 12.10 (5.16) 0.022
R_FARpenNUM_NO (mean (sd)) 12.26 (5.99) 8.65 (4.57) 0.034
R_FAR_C_NUM_NO (mean (sd)) 5.09 (2.27) 5.55 (2.80) 0.553
R_FAR_floorsolidNUM_NO (mean (sd)) 6.04 (3.65) 6.75 (3.21) 0.507
R_BR_area2_NUM (mean (sd)) 18.26 (9.99) 16.10 (8.85) 0.460
R_PR_areaNUM (mean (sd)) 10.48 (7.88) 12.40 (6.16) 0.383
OUT_SOW_mort_proNUM (mean (sd)) 4.74 (2.12) 13.35 (3.27) <0.001
OUT_SOW_totremproNUM (mean (sd)) 8.91 (5.80) 17.85 (8.03) <0.001
OUT_SOW_cullproNUM (mean (sd)) 11.78 (6.69) 15.35 (8.20) 0.124
R_BR_sowspersection (%) 0.509
<20 2 ( 8.7) 1 ( 5.0)
>100 3 (13.0) 3 ( 15.0)
20-50 5 (21.7) 6 ( 30.0)
50-100 9 (39.1) 7 ( 35.0)
all 4 (17.4) 1 ( 5.0)
noinfo 0 ( 0.0) 2 ( 10.0)
R_BR_type (%) 0.334
1 3 (13.0) 1 ( 5.0)
2 1 ( 4.3) 1 ( 5.0)
3 15 (65.2) 16 ( 80.0)
12 2 ( 8.7) 0 ( 0.0)
13 2 ( 8.7) 0 ( 0.0)
23 0 ( 0.0) 1 ( 5.0)
124 0 ( 0.0) 1 ( 5.0)
R_BR_noise = 1 (%) 13 (56.5) 9 ( 45.0) 0.654
R_BR_pest_NO = 1 (%) 10 (43.5) 11 ( 55.0) 0.654
R_BR_airqual = 1 (%) 7 (30.4) 11 ( 55.0) 0.187
R_BR_outC (%) 0.374
13 (56.5) 10 ( 50.0)
10 1 ( 4.3) 0 ( 0.0)
13 1 ( 4.3) 0 ( 0.0)
14 0 ( 0.0) 2 ( 10.0)
15 1 ( 4.3) 0 ( 0.0)
18 1 ( 4.3) 0 ( 0.0)
19 1 ( 4.3) 1 ( 5.0)
2 0 ( 0.0) 1 ( 5.0)
20 0 ( 0.0) 1 ( 5.0)
23 0 ( 0.0) 1 ( 5.0)
24 0 ( 0.0) 1 ( 5.0)
25 4 (17.4) 1 ( 5.0)
28,3 0 ( 0.0) 1 ( 5.0)
31 0 ( 0.0) 1 ( 5.0)
8 1 ( 4.3) 0 ( 0.0)
R_BR_floorbetmetplastwoodother = betmet (%) 4 (17.4) 8 ( 40.0) 0.191
R_BR_floorsolid_0981_2 = 2 (%) 5 (21.7) 2 ( 10.0) 0.531
R_BR_kuivaliete (%) 0.769
1 6 (26.1) 4 ( 20.0)
2 15 (65.2) 15 ( 75.0)
12 2 ( 8.7) 1 ( 5.0)
R_BR_PREGsame = 1 (%) 8 (34.8) 4 ( 21.1) 0.524
R_PR_areapersow (%) 0.593
1 4 (17.4) 7 ( 35.0)
2 8 (34.8) 5 ( 25.0)
3 5 (21.7) 3 ( 15.0)
4 6 (26.1) 5 ( 25.0)
R_PR_nonoise = 1 (%) 12 (52.2) 12 ( 60.0) 0.836
R_PR_air = 1 (%) 2 ( 8.7) 9 ( 45.0) 0.018
R_PR_floorsolid_0791_2 = 2 (%) 12 (52.2) 5 ( 25.0) 0.132
R_PR_bedmatyn = 1 (%) 18 (78.3) 12 ( 60.0) 0.333
R_PR_bedmatamount (%) 0.387
0 5 (21.7) 8 ( 40.0)
1 9 (39.1) 4 ( 20.0)
2 5 (21.7) 3 ( 15.0)
3 4 (17.4) 5 ( 25.0)
R_PR_floornoslip = 1 (%) 3 (13.0) 2 ( 10.0) 1.000
R_PR_dirtmed = 2 (%) 9 (42.9) 9 ( 50.0) 0.901
R_FAR_pensize_med4.9 (%) 0.340
0 ( 0.0) 1 ( 5.0)
1,0 11 (47.8) 12 ( 60.0)
2,0 12 (52.2) 7 ( 35.0)
R_FAR_noise = 1 (%) 11 (47.8) 8 ( 40.0) 0.836
R_FAR_airqual = 1 (%) 2 ( 8.7) 2 ( 10.0) 1.000
R_FAR_floorsolid_all0_100_100_2_muu1 (%) 0.132
0 6 (26.1) 5 ( 25.0)
1 13 (56.5) 15 ( 75.0)
2 4 (17.4) 0 ( 0.0)
R_FAR_kuivaliete = 2 (%) 17 (73.9) 19 ( 95.0) 0.146
R_FAR_kunto = 1 (%) 1 ( 4.3) 3 ( 15.0) 0.501
R_FAR_nonslippery = 1 (%) 5 (21.7) 2 ( 10.0) 0.531
OUT_SOW_mort_dic = 1 (%) 0 ( 0.0) 20 (100.0) <0.001
OUT_SOW_totrem_dic = 1 (%) 7 (30.4) 15 ( 75.0) 0.009
OUT_SOW_cull_dic = 1 (%) 11 (47.8) 10 ( 50.0) 1.000

Yhteenveto poistojen mediaanin mukaan

#This is a very hacky function. If used within an RMarkdown document, KreateTableOne should be #called in a code chunk with \code{results='hide'} to hide the plain test results printed from #\code{tableone::CreateTableOne}. The resulting data frame should be saved as an object and used #in a second code chunk for formatted printing. Suggestions for improvement are welcomed.


table2 = KreateTableOne(x=meddesc, factorVars=colnames(medcat), strata='OUT_SOW_cull_dic')
table2%>%
    kable("html", align = "rrr", caption = "Data variable summary strat by Sow cull") %>%
  kable_styling(bootstrap_options = c("hover", "condensed")) %>% 
  scroll_box(height = "300px" )
Data variable summary strat by Sow cull
0 1 p test
n 22 21
R_BR_C_NUM_NO (mean (sd)) 15.50 (9.06) 13.62 (7.33) 0.460
R_BR_floorsolid_NUM_NO (mean (sd)) 8.09 (2.62) 7.67 (2.65) 0.600
R_PR_sectionsNUM_NO (mean (sd)) 2.00 (1.51) 1.95 (1.43) 0.913
R_PR_sowsinsecNUM_NO (mean (sd)) 18.59 (10.13) 18.05 (9.90) 0.860
R_PR_sowsNUM_NO (mean (sd)) 11.32 (5.75) 9.33 (5.99) 0.274
R_PR_areapersow_NUM_NO (mean (sd)) 11.59 (6.02) 11.14 (7.04) 0.823
R_PR_crareaNUM_NO (mean (sd)) 6.68 (4.52) 6.48 (5.04) 0.889
R_PR_CNUM_NO (mean (sd)) 7.32 (3.98) 6.14 (2.35) 0.248
R_PR_floorsolidNUM_NO (mean (sd)) 9.73 (4.48) 9.38 (4.38) 0.799
R_PR_dirtNUM_NO (mean (sd)) 2.75 (2.31) 3.11 (2.11) 0.620
R_FAR_secNUM_NO (mean (sd)) 3.00 (2.23) 4.33 (2.63) 0.080
R_FAR_pensinsecNUM_NO (mean (sd)) 9.36 (4.30) 10.95 (6.17) 0.331
R_FARpenNUM_NO (mean (sd)) 10.45 (5.03) 10.71 (6.29) 0.882
R_FAR_C_NUM_NO (mean (sd)) 5.23 (2.86) 5.38 (2.16) 0.844
R_FAR_floorsolidNUM_NO (mean (sd)) 6.82 (3.54) 5.90 (3.33) 0.389
R_BR_area2_NUM (mean (sd)) 18.41 (9.50) 16.05 (9.44) 0.418
R_PR_areaNUM (mean (sd)) 12.45 (6.72) 10.24 (7.50) 0.313
OUT_SOW_mort_proNUM (mean (sd)) 8.73 (5.55) 8.76 (4.73) 0.983
OUT_SOW_totremproNUM (mean (sd)) 7.77 (5.46) 18.62 (6.82) <0.001
OUT_SOW_cullproNUM (mean (sd)) 7.18 (3.57) 20.00 (4.27) <0.001
R_BR_sowspersection (%) 0.821
<20 1 ( 4.5) 2 ( 9.5)
>100 3 (13.6) 3 ( 14.3)
20-50 5 (22.7) 6 ( 28.6)
50-100 8 (36.4) 8 ( 38.1)
all 4 (18.2) 1 ( 4.8)
noinfo 1 ( 4.5) 1 ( 4.8)
R_BR_type (%) 0.542
1 1 ( 4.5) 3 ( 14.3)
2 2 ( 9.1) 0 ( 0.0)
3 16 (72.7) 15 ( 71.4)
12 1 ( 4.5) 1 ( 4.8)
13 1 ( 4.5) 1 ( 4.8)
23 0 ( 0.0) 1 ( 4.8)
124 1 ( 4.5) 0 ( 0.0)
R_BR_noise = 1 (%) 10 (45.5) 12 ( 57.1) 0.645
R_BR_pest_NO = 1 (%) 11 (50.0) 10 ( 47.6) 1.000
R_BR_airqual = 1 (%) 12 (54.5) 6 ( 28.6) 0.157
R_BR_outC (%) 0.366
10 (45.5) 13 ( 61.9)
10 0 ( 0.0) 1 ( 4.8)
13 1 ( 4.5) 0 ( 0.0)
14 1 ( 4.5) 1 ( 4.8)
15 0 ( 0.0) 1 ( 4.8)
18 1 ( 4.5) 0 ( 0.0)
19 0 ( 0.0) 2 ( 9.5)
2 1 ( 4.5) 0 ( 0.0)
20 1 ( 4.5) 0 ( 0.0)
23 1 ( 4.5) 0 ( 0.0)
24 0 ( 0.0) 1 ( 4.8)
25 4 (18.2) 1 ( 4.8)
28,3 0 ( 0.0) 1 ( 4.8)
31 1 ( 4.5) 0 ( 0.0)
8 1 ( 4.5) 0 ( 0.0)
R_BR_floorbetmetplastwoodother = betmet (%) 6 (27.3) 6 ( 28.6) 1.000
R_BR_floorsolid_0981_2 = 2 (%) 3 (13.6) 4 ( 19.0) 0.946
R_BR_kuivaliete (%) 0.185
1 4 (18.2) 6 ( 28.6)
2 15 (68.2) 15 ( 71.4)
12 3 (13.6) 0 ( 0.0)
R_BR_PREGsame = 1 (%) 7 (33.3) 5 ( 23.8) 0.733
R_PR_areapersow (%) 0.108
1 6 (27.3) 5 ( 23.8)
2 5 (22.7) 8 ( 38.1)
3 7 (31.8) 1 ( 4.8)
4 4 (18.2) 7 ( 33.3)
R_PR_nonoise = 1 (%) 11 (50.0) 13 ( 61.9) 0.632
R_PR_air = 1 (%) 5 (22.7) 6 ( 28.6) 0.929
R_PR_floorsolid_0791_2 = 2 (%) 10 (45.5) 7 ( 33.3) 0.617
R_PR_bedmatyn = 1 (%) 16 (72.7) 14 ( 66.7) 0.920
R_PR_bedmatamount (%) 0.863
0 6 (27.3) 7 ( 33.3)
1 6 (27.3) 7 ( 33.3)
2 5 (22.7) 3 ( 14.3)
3 5 (22.7) 4 ( 19.0)
R_PR_floornoslip = 1 (%) 1 ( 4.5) 4 ( 19.0) 0.314
R_PR_dirtmed = 2 (%) 8 (40.0) 10 ( 52.6) 0.639
R_FAR_pensize_med4.9 (%) 0.491
0 ( 0.0) 1 ( 4.8)
1,0 13 (59.1) 10 ( 47.6)
2,0 9 (40.9) 10 ( 47.6)
R_FAR_noise = 1 (%) 12 (54.5) 7 ( 33.3) 0.274
R_FAR_airqual = 1 (%) 2 ( 9.1) 2 ( 9.5) 1.000
R_FAR_floorsolid_all0_100_100_2_muu1 (%) 0.379
0 4 (18.2) 7 ( 33.3)
1 15 (68.2) 13 ( 61.9)
2 3 (13.6) 1 ( 4.8)
R_FAR_kuivaliete = 2 (%) 19 (86.4) 17 ( 81.0) 0.946
R_FAR_kunto = 1 (%) 1 ( 4.5) 3 ( 14.3) 0.566
R_FAR_nonslippery = 1 (%) 4 (18.2) 3 ( 14.3) 1.000
OUT_SOW_mort_dic = 1 (%) 10 (45.5) 10 ( 47.6) 1.000
OUT_SOW_totrem_dic = 1 (%) 5 (22.7) 17 ( 81.0) <0.001
OUT_SOW_cull_dic = 1 (%) 0 ( 0.0) 21 (100.0) <0.001

Yhteenveto joku hylkays mukaan

res_mca = MCA(medmca, quanti.sup = c(27,28),quali.sup=c(25:26), graph = FALSE)
summary(res_mca)
## 
## Call:
## MCA(X = medmca, quanti.sup = c(27, 28), quali.sup = c(25:26),  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6
## Variance               0.277   0.148   0.121   0.103   0.094   0.084
## % of var.             15.813   8.477   6.920   5.881   5.373   4.809
## Cumulative % of var.  15.813  24.289  31.210  37.091  42.464  47.273
##                        Dim.7   Dim.8   Dim.9  Dim.10  Dim.11  Dim.12
## Variance               0.079   0.077   0.071   0.066   0.057   0.054
## % of var.              4.537   4.415   4.030   3.798   3.256   3.107
## Cumulative % of var.  51.810  56.225  60.255  64.053  67.310  70.416
##                       Dim.13  Dim.14  Dim.15  Dim.16  Dim.17  Dim.18
## Variance               0.054   0.053   0.044   0.043   0.041   0.036
## % of var.              3.088   3.001   2.536   2.438   2.329   2.052
## Cumulative % of var.  73.504  76.505  79.041  81.479  83.808  85.860
##                       Dim.19  Dim.20  Dim.21  Dim.22  Dim.23  Dim.24
## Variance               0.031   0.029   0.026   0.024   0.023   0.018
## % of var.              1.760   1.654   1.461   1.382   1.290   1.056
## Cumulative % of var.  87.620  89.275  90.735  92.118  93.407  94.463
##                       Dim.25  Dim.26  Dim.27  Dim.28  Dim.29  Dim.30
## Variance               0.018   0.014   0.012   0.010   0.010   0.007
## % of var.              1.034   0.819   0.679   0.596   0.550   0.393
## Cumulative % of var.  95.497  96.316  96.995  97.592  98.142  98.534
##                       Dim.31  Dim.32  Dim.33  Dim.34  Dim.35  Dim.36
## Variance               0.007   0.006   0.004   0.003   0.002   0.002
## % of var.              0.380   0.339   0.244   0.182   0.121   0.087
## Cumulative % of var.  98.914  99.254  99.498  99.679  99.800  99.888
##                       Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               0.001   0.000   0.000   0.000   0.000   0.000
## % of var.              0.065   0.023   0.016   0.008   0.000   0.000
## Cumulative % of var.  99.953  99.975  99.992 100.000 100.000 100.000
## 
## Individuals (the 10 first)
##                         Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                    | -0.514  2.220  0.143 |  0.125  0.246  0.008 |
## 2                    |  0.698  4.098  0.223 | -0.245  0.942  0.027 |
## 3                    |  0.950  7.584  0.540 |  0.244  0.937  0.036 |
## 4                    | -0.632  3.352  0.369 |  0.149  0.349  0.021 |
## 5                    | -0.536  2.418  0.073 |  1.436 32.332  0.524 |
## 6                    |  1.155 11.204  0.587 |  0.196  0.600  0.017 |
## 7                    | -0.569  2.721  0.179 |  0.159  0.397  0.014 |
## 8                    |  0.153  0.197  0.027 |  0.187  0.551  0.041 |
## 9                    | -0.028  0.007  0.001 | -0.457  3.276  0.208 |
## 10                   |  0.008  0.000  0.000 | -0.498  3.893  0.215 |
##                       Dim.3    ctr   cos2  
## 1                     0.292  1.639  0.046 |
## 2                     0.624  7.475  0.178 |
## 3                     0.036  0.025  0.001 |
## 4                     0.141  0.381  0.018 |
## 5                    -0.231  1.029  0.014 |
## 6                     0.365  2.564  0.059 |
## 7                     0.598  6.872  0.198 |
## 8                    -0.119  0.273  0.016 |
## 9                    -0.353  2.391  0.124 |
## 10                    0.025  0.012  0.001 |
## 
## Categories (the 10 first)
##                         Dim.1    ctr   cos2 v.test    Dim.2    ctr   cos2
## <20                  |  1.753  3.228  0.230  3.111 |  0.664  0.865  0.033
## >100                 | -0.396  0.329  0.025 -1.032 | -0.723  2.046  0.085
## 20-50                |  0.030  0.004  0.000  0.116 | -0.313  0.703  0.034
## 50-100               | -0.490  1.346  0.142 -2.446 | -0.088  0.080  0.005
## all                  |  1.318  3.042  0.229  3.099 |  0.206  0.138  0.006
## noinfo               | -0.984  0.678  0.047 -1.408 |  3.079 12.383  0.462
## R_BR_type_1          |  1.606  3.613  0.265  3.334 |  0.126  0.042  0.002
## R_BR_type_2          |  1.902  2.534  0.176  2.723 |  0.367  0.176  0.007
## R_BR_type_3          | -0.422  1.936  0.461 -4.399 | -0.046  0.043  0.005
## R_BR_type_12         |  0.829  0.482  0.034  1.187 | -0.363  0.172  0.006
##                      v.test    Dim.3    ctr   cos2 v.test  
## <20                   1.179 | -0.393  0.370  0.012 -0.697 |
## >100                 -1.886 | -0.299  0.429  0.014 -0.780 |
## 20-50                -1.189 |  0.377  1.251  0.049  1.433 |
## 50-100               -0.437 | -0.028  0.010  0.000 -0.141 |
## all                   0.484 |  0.293  0.343  0.011  0.688 |
## noinfo                4.407 | -1.094  1.914  0.058 -1.565 |
## R_BR_type_1           0.262 |  0.946  2.862  0.092  1.963 |
## R_BR_type_2           0.525 | -1.015  1.650  0.050 -1.453 |
## R_BR_type_3          -0.479 |  0.002  0.000  0.000  0.023 |
## R_BR_type_12         -0.520 |  1.054  1.777  0.054  1.509 |
## 
## Categorical variables (eta2)
##                                  Dim.1 Dim.2 Dim.3  
## R_BR_sowspersection            | 0.573 0.577 0.126 |
## R_BR_type                      | 0.610 0.057 0.391 |
## R_BR_noise                     | 0.085 0.229 0.071 |
## R_BR_airqual                   | 0.115 0.213 0.004 |
## R_BR_floorbetmetplastwoodother | 0.170 0.000 0.046 |
## R_BR_floorsolid_0981_2         | 0.620 0.040 0.013 |
## R_BR_kuivaliete                | 0.559 0.028 0.136 |
## R_BR_PREGsame                  | 0.305 0.142 0.188 |
## R_PR_areapersow                | 0.407 0.238 0.181 |
## R_PR_nonoise                   | 0.028 0.272 0.074 |
## 
## Supplementary categories
##                         Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## OUT_SOW_totrem_dic_0 |  0.343  0.113  2.174 | -0.125  0.015 -0.792 |
## OUT_SOW_totrem_dic_1 | -0.328  0.113 -2.174 |  0.119  0.015  0.792 |
## OUT_SOW_cull_dic_0   |  0.050  0.003  0.334 | -0.031  0.001 -0.202 |
## OUT_SOW_cull_dic_1   | -0.053  0.003 -0.334 |  0.032  0.001  0.202 |
##                       Dim.3   cos2 v.test  
## OUT_SOW_totrem_dic_0 -0.030  0.001 -0.192 |
## OUT_SOW_totrem_dic_1  0.029  0.001  0.192 |
## OUT_SOW_cull_dic_0   -0.138  0.020 -0.913 |
## OUT_SOW_cull_dic_1    0.144  0.020  0.913 |
## 
## Supplementary categorical variables (eta2)
##                        Dim.1 Dim.2 Dim.3  
## OUT_SOW_totrem_dic   | 0.113 0.015 0.001 |
## OUT_SOW_cull_dic     | 0.003 0.001 0.020 |
## 
## Supplementary continuous variables
##                         Dim.1    Dim.2    Dim.3  
## OUT_SOW_mort_proNUM  | -0.379 |  0.166 | -0.085 |
## OUT_SOW_cullproNUM   | -0.083 |  0.095 |  0.101 |

To visualize the percentage of inertia explained by each MCA dimension:

eig.val <- res_mca$eig
barplot(eig.val[, 2], 
        names.arg = 1:nrow(eig.val), 
        main = "Variances Explained by Dimensions (%)",
        xlab = "Principal Dimensions",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eig.val), eig.val[, 2], 
      type = "b", pch = 19, col = "red")

res <- explor::prepare_results(res_mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = NULL)
res <- explor::prepare_results(res_mca)
explor::MCA_ind_plot(res, xax = 1, yax = 2,ind_sup = FALSE,
    lab_var = NULL, , ind_lab_min_contrib = 0,
    col_var = NULL, labels_size = 9,
    point_opacity = 0.5, opacity_var = NULL, point_size = 64,
    ellipses = FALSE, transitions = TRUE, labels_positions = NULL)
fviz_mca_var(res_mca, choice = "quanti.sup",
             ggtheme = theme_minimal())

## ```{r, echo = FALSE}
## res.hcpc = HCPC(res, nb.clust = -1, graph = FALSE)
## ```

## 
## ```
## drawn <-
## c("6", "5", "3", "39", "18", "4", "27", "17", "13", "21")
## par(mar = c(4.1, 4.1, 1.1, 2.1))
## plot.HCPC(res.hcpc, choice = 'map', draw.tree = FALSE, select = drawn, title = '')
## ```
## 
## **Figure - Ascending Hierarchical Classification of the individuals.**
## *The classification made on individuals reveals 3 clusters.*
## 
## 
## The cluster 1 is made of individuals such as*. This group is characterized by4* and *4*. :
## 
## - high frequency for factors like *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_0*, *R_PR_air=R_PR_air_1*, *R_BR_type=R_BR_type_3*, *R_FAR_pensize_med4.9=1,0*, *R_FAR_airqual=R_FAR_airqual_1*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1* and *R_BR_PREGsame=R_BR_PREGsame_0* (factors are sorted from the most common).
## - low frequency for factors like *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_FAR_pensize_med4.9=2,0*, *R_PR_air=R_PR_air_0*, *R_BR_PREGsame=R_BR_PREGsame_1*, *R_FAR_airqual=R_FAR_airqual_0*, *OUT_SOW_totrem_dic=OUT_SOW_totrem_dic_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_bedmatamount=R_PR_bedmatamount_1* and *R_PR_bedmatamount=R_PR_bedmatamount_2* (factors are sorted from the rarest).
## 
## The 1st cluster is made of individuals such as *17*. This group is characterized by :
## 
## - high frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_1*, *R_PR_air=R_PR_air_0*, *R_PR_bedmatamount=R_PR_bedmatamount_3*, *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1* and *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_1* (factors are sorted from the most common).
## - low frequency for the factors *R_PR_bedmatyn=R_PR_bedmatyn_0*, *R_PR_bedmatamount=R_PR_bedmatamount_0*, *R_PR_air=R_PR_air_1*, *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_FAR_kuivaliete=R_FAR_kuivaliete_1* and *R_BR_kuivaliete=R_BR_kuivaliete_1* (factors are sorted from the rarest).
## 
## The cluster 3 is made of individuals such as*. This group is characterized by3* and *3*. :
## 
## - high frequency for factors like *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_2*, *R_BR_kuivaliete=R_BR_kuivaliete_1*, *R_FAR_kuivaliete=R_FAR_kuivaliete_1*, *R_PR_dirtmed=R_PR_dirtmed_1*, *R_FAR_floorsolid_all0_100_100_2_muu1=R_FAR_floorsolid_all0_100_100_2_muu1_2*, *R_BR_type=R_BR_type_1*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_2*, *R_PR_areapersow=R_PR_areapersow_4*, *R_BR_sowspersection=all* and *R_BR_sowspersection=<20* (factors are sorted from the most common).
## - low frequency for factors like *R_BR_floorsolid_0981_2=R_BR_floorsolid_0981_2_1*, *R_BR_kuivaliete=R_BR_kuivaliete_2*, *R_BR_type=R_BR_type_3*, *R_FAR_kuivaliete=R_FAR_kuivaliete_2*, *R_PR_floorsolid_0791_2=R_PR_floorsolid_0791_2_1*, *R_PR_dirtmed=R_PR_dirtmed_2*, *R_BR_sowspersection=50-100*, *R_BR_PREGsame=R_BR_PREGsame_0*, *R_PR_bedmatyn=R_PR_bedmatyn_0* and *R_PR_bedmatamount=R_PR_bedmatamount_0* (factors are sorted from the rarest).
## **Results for the Hierarchical Clustering on Principal Components**
##    name                   
## 1  "$data.clust"          
## 2  "$desc.var"            
## 3  "$desc.var$test.chi2"  
## 4  "$desc.axes$category"  
## 5  "$desc.axes"           
## 6  "$desc.axes$quanti.var"
## 7  "$desc.axes$quanti"    
## 8  "$desc.ind"            
## 9  "$desc.ind$para"       
## 10 "$desc.ind$dist"       
## 11 "$call"                
## 12 "$call$t"              
##    description                                              
## 1  "dataset with the cluster of the individuals"            
## 2  "description of the clusters by the variables"           
## 3  "description of the cluster var. by the categorical var."
## 4  "description of the clusters by the categories."         
## 5  "description of the clusters by the dimensions"          
## 6  "description of the cluster var. by the axes"            
## 7  "description of the clusters by the axes"                
## 8  "description of the clusters by the individuals"         
## 9  "parangons of each clusters"                             
## 10 "specific individuals"                                   
## 11 "summary statistics"                                     
## 12 "description of the tree"